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Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset Nick Rossenbach∗†, Robin Schmitt∗†, Tina Raissi∗, Simon Berger∗†, Larissa Kleppel∗, Ralf Schlüter∗† ∗RWTH Aachen University, †AppTek.ai Aachen, Germany {lastname}@ml.rwth-aachen.de Abstract The recently published Loquacious dataset aims to be a replacement for established English automatic speech recognition (ASR) datasets such as LibriSpeech or TED-Lium. The main goal of the Loquacious dataset is to provide properly defined training and test partitions across many acoustic and language domains, with an open license suitable for both academia and industry. To further promote the benchmarking and usability of this new dataset, we present additional resources in the form of n-gram language models (LMs), a grapheme-to-phoneme (G2P) model and pronunciation lexica, with open and public access. Utilizing those additional resources we show experimental results across a wide range of ASR architectures with different label units and topologies. Our initial experimen- tal results indicate that the Loquacious dataset offers a valuable study case for a variety of common challenges in ASR. Keywords: Loquacious, language model, pronunciation lexicon, speech recognition, reference baselines 1. Introduction In order to compare ASR systems across scientific publications, it is necessary to clearly define the training and testing conditions and data used. For this purpose, many benchmarks and correspond- ing datasets have been published in the past. No- table datasets for academic use covering English language are: Switchboard (Godfrey et al., 1992), LibriSpeech (Panayotov et al., 2015) or TED-Lium (Hernandez et al., 2018). Especially LibriSpeech, with more than 1700 citations in 2024 alone1, is dominating ASR research. However, due to the increase in computation power, more and more re- search is performed without any constraints on the used data. This generally yields better results, but limits scientific comparability. On the other hand, existing datasets only provide a smaller amount of data, and are often not realistic in the diversity of au- dio and text conditions. Also, some datasets are not directly accessible, were removed from public ac- cess after publication2, or have restricted licenses. The Loquacious dataset (Parcollet et al., 2025) was published with the idea in mind to replace older datasets, offering multiple benefits. The important highlights are: • A diverse selection of data sources containing both read and spontaneous speech • Three different training subsets, consisting of 250, 2.5k, and 25k hours, with 250 and 2.5k having uniformly balanced data sources • Defined dev and test sets for every data source 1according to Google Scholar, 15th Oct. 2025 2The TED terms of use disallow machine learning usage, existing datasets based on TED were taken offline • A strong and consistent text normalization • Open licensing for academia and industry Such aspects make the Loquacious dataset a promising replacement for older standard datasets, with the 2.5k hours subset having a similar magni- tude as the 960 hours of LibriSpeech. Moreover, due to similar text normalization, it is straightfor- ward to use the dataset with existing LibriSpeech- oriented pipelines. As the original release of Loquacious only in- cluded training and test data, we aim to extend the available material with count-based LMs, pro- nunciation lexica, and pre-trained G2P models. Such resources exist for LibriSpeech3 and other datasets, and are relevant for the comparison of ASR pipelines that require such resources. The introduction of a pronunciation lexicon allows for explicit decoder representations via lexical trees or finite state machines. These representations can integrate word-level and lexical information together with model specific label topologies (Mohri et al., 2002). This type of constraint could benefit the ASR performance, for example under domain mismatch conditions (Raissi et al., 2025). Moreover, the align- ment of models using a blank-free label topology with phonemic units and silence modeling offers the advantage of providing word and phoneme bound- aries unambiguously. Prior works also show that phoneme-based models are able to provide align- ments with more accurate time information relative to the evidence in the signal, compared to the byte- pair encoding (BPE) based models (Rousso et al., 2024; Jiang et al., 2023). 3https://openslr.org/11/ arXiv:2512.17915v1 [cs.CL] 27 Nov 2025
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https://arxiv.org/pdf/2512.17915
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2512.17915
| 1
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1.1. Contributions With this work, we benchmark several ASR archi- tectures with different label topologies using BPE and phoneme label units. We show the effect of different decoding methods, with open or closed vocabulary and optional use of a LM. For this pur- pose, we extend the Loquacious dataset with a pronunciation lexicon, a Sequitur (Bisani and Ney, 2008) based G2P model and pre-trained n-gram count-based LMs. All resources are available as part of the official Loquacious repository on Hug- gingFace4. The scripts for creating the resources are available on Github5. We provide results using lexicon constrained and 4-gram LM decoding across many different ASR architectures for the 250 hours and 2.5k hours sub- sets of Loquacious. In addition, we include addi- tional ablation studies regarding the pronunciation lexicon and the effect of pronunciation variants. As the original publication for Loquacious mostly con- tained results without data augmentation, we inves- tigate the effect of SpecAugment (Park et al., 2019) and speed perturbation (Ko et al., 2015). Finally, we discuss additional insights we gained about this new dataset while working with it. 2. Language Modeling and Lexicon Despite the prominence of neural (large) language models in ASR, count-based LMs still have their advantages as they are fast to train and need of- ten only negligible computational resources. Just recently NVidia explored GPU support for count- based LMs (Bataev et al., 2025). Count-based LMs are suitable for resource efficient analysis of how well training data is matching the test data. 2.1. Vocabulary Word-level LMs require a fixed vocabulary. We aim for a size that is similar to that of the official lexicon for LibriSpeech, which includes 200k words. In order to avoid an arbitrary cutoff, we choose the following process to determine the vocabulary: 1. We use the CMU Pronunciation Dictionary (CMUdict)6 0.7b as initial starting point for our vocab, which contains pronunciations for about 124k words, with 134k total pronunciations. 2. We select every word from the CMUdict that is appearing at least once in the full text of 4https://huggingface.co/datasets/ speechbrain/LoquaciousSet 5https://github.com/rwth-i6/ LoquaciousAdditionalResources 6http://www.speech.cs.cmu.edu/cgi-bin/ cmudict the Loqacious train-large training data. This results in 95k words. 3. We then select every word that appears at least 4 times in the train-large training data, which gives us 197k words. 4. The union of both results in 216k words which we use as target vocabulary. 2.2. Pronunciation Lexicon Characters or sub-word methods such as BPE (Sennrich et al., 2016) or Sentencepieces via uni- gram LM (Kudo, 2018) nowadays are common for target label representations in ASR. To addition- ally allow for phoneme representations, we create phoneme pronunciations for all words in the vocab- ulary file as follows: 1. For all words that are part of the CMUdict, we take the existing entries (with one or more pro- nunciations) and remove stress markers. 2. We train a 5th-order G2P model with Sequitur us- ing the full CMUdict as training data. For higher orders, we see no further improvement on our held-out cross-validation set. 3. We generate two pronunciation variants using Sequitur for all words which are not in the CMU- dict. For the last step, we either take only the most prob- able variant, or we select the second variant based on a probability threshold which is set to either 60% or 80%. We only consider a second pronuncia- tion variant generated if the re-normalized posterior probability of the first variant is below this threshold. 2.3. Count-based Language Models We use the KenLM toolkit (Heafield, 2011) to train ARPA-format count-based language models. We create LMs in three different settings: 1. 3rd-order with singleton pruning of 3-grams. 2. 4th-order with singleton pruning of 3-grams and 4-grams. 3. 4th-order without any pruning. The perplexities and OOV rates can be found in Table 1. As expected, every test-set has different perplexities, indicating both the complexity of the text and how much fitting training data is available within the full Loquacious dataset. 3. Automatic Speech Recognition The original Loquacious publication only listed re- sults for ASR systems that combine attention en- coder decoder (AED) (Chan et al., 2016) and con- nectionist temporal classification (CTC) (Graves et al., 2006). We use a set of different systems
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https://arxiv.org/pdf/2512.17915
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2512.17915
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Table 1: Perplexities and OOV percentage of the different count-based LMs on the respective dev sets. All LMs are restricted to the 216k words vocabulary. Language Model n-gram count Loquacious Commonvoice LibriSpeech VoxPopuli Yodas OOV[%] PPL OOV[%] PPL OOV[%] PPL OOV[%] PPL OOV[%] PPL 3-gram pruned 36M 0.58 222 1.25 361 0.58 231 0.13 154 0.24 201 4-gram pruned 54M 0.58 202 1.25 327 0.58 211 0.13 139 0.24 187 + LibriSpeech 201M 0.58 196 1.27 352 0.58 149 0.13 173 0.24 243 4-gram unpruned 246M 0.58 193 1.25 311 0.58 195 0.13 135 0.24 182 with different training pipelines to verify our lexi- con and n-gram LMs, as well as showing open- vocabulary results and other ablation studies. ASR systems using CTC loss only have an encoder with independent label outputs. Such systems tend to profit the most from additional LMs as they have no explicit language context modeling. For compar- ison, we include recurrent neural network trans- ducer (RNN-T)-based (Graves, 2012) ASR sys- tems. They provide a decoder structure for explicit language context modeling, so that additional lan- guage model information might be less relevant. To include a setup which properly supports pronun- ciation variants, we also include a factored hybrid (FH) model (Raissi et al., 2020). FH is a modern alternative to the classic hybrid neural network hid- den Markov model (HMM) (Bourlard and Morgan, 1993), being trained from scratch and thus avoid- ing both initial alignments from Gaussian mixture HMMs and decision tree based phone clustering. The diphone FH is trained similarly to CTC but of- fers a blank-free label topology with phonemic units and diphone context modeling. Finally, we include an AED system to be able compare to the original Loquacious publication, even if in our case it does not support word level count-LM decoding. 3.1. General Settings The ASR systems in this work are based on differ- ent software and settings, covering a broader range of different pipelines. All neural models are trained using RETURNN (Zeyer et al., 2018) or variants of it7. The FH system pipeline uses a Tensorflow back- end, while all other models are based on PyTorch. All experiment pipelines are based on Sisyphus (Peter et al., 2018). Prefix-tree based decoding is performed using Flashlight (Kahn et al., 2022) via the torchaudio interface for CTC and RASR (Rybach et al., 2011) for (m)RNN-T and FH. All systems use a 12-layer Conformer (Gulati et al., 2020) encoder with a hidden size of 512. We use 80-dimensional log-mel spectrograms with a win- dow size of 25ms and a shift of 10ms including 7https://github.com/JackTemaki/ MiniReturnn SpecAugment and speed perturbation, with excep- tion of FH that uses only SpecAugment. The fea- tures are down-sampled with two convolutional lay- ers by a factor of six for RNN-T and AED and four for the other systems. We train each system for 100 epochs on the 250 hours train.small subset and for 40 epochs on the 2.5k hours train.medium subset. The training pipelines include different vari- ants of Adam and one-cycle-learning-rate (OCLR) scheduling. We evaluate each model on the full dev and test sets, and in many cases report the word error rates (WER) individually for the 4 dif- ferent test sets. For any system using a LM, we perform tuning of the LM and label prior correction scales on a fixed random subset of the full dev set. When using phonemes, we use an additional label set for end-of-word phonemes. 3.2. CTC The CTC system has a total parameter count of 77M. For the BPE-based systems, three different decoding modes are used: 1. Simple greedy decoding by picking the position-wise maximum label 2. Time-synchronous beam-search. 3. Time-synchronous beam-search with lexical prefix tree and optional 4-gram LM. Phoneme labels are only supported when using prefix tree based decoding. 3.3. Full-Context RNN-T The standard RNN-T system is built analogously to the CTC system. The prediction network consists of a single LSTM layer with a hidden dimension of 512. The joiner network is a single linear mapping with a ReLU activation function and an output dimension of 640, yielding a total parameter count of 80M. We use two different decoding modes for the BPE version: 1. Time-synchronous beam-search. 2. Time-synchronous beam-search with lexical prefix tree and optional 4-gram LM including zero-encoder internal language model correc- tion (Variani et al., 2020; Meng et al., 2021).
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https://arxiv.org/pdf/2512.17915
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2512.17915
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2512.17915_page_4
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Table 2: Recognition results for BPE ASR systems that work without additional LMs or lexicon. Abbrevia- tions for test sets: Commonvoice (CV), LibriSpeech (LS), VoxPopuli (VP), Yodas (YD). Data Architecture Decoding WER [%] Loquacious CV LS VP YD dev test test train.small 250h CTC Greedy 17.2 18.6 30.1 16.7 12.5 21.1 RNN-T 15.2 16.1 26.8 14.1 11.0 17.9 Beam-Search 14.9 15.9 26.3 13.9 10.9 17.6 AED Greedy 15.3 16.7 28.0 14.7 10.8 20.0 Beam-Search 14.7 16.0 26.8 14.2 10.4 18.0 train.medium 2500h CTC Greedy 10.6 11.5 19.3 9.0 8.1 15.4 RNN-T 9.0 10.3 16.1 7.2 7.0 21.9 Beam-Search 8.4 9.3 15.4 7.0 6.9 12.4 AED Greedy 8.7 10.0 16.5 7.5 7.0 15.1 Beam-Search 8.4 9.5 15.9 7.3 7.0 13.1 3.4. Context-1 Monotonic RNN-T The monotonic transducer (Tripathi et al., 2019) uses a similar encoder to the CTC and RNN-T mod- els. It has a context limited to just one history label which is embedded with an embedding size of 256 and then forwarded through a feed-forward pre- diction network with 2 layers of size 640 and tanh activation. The joiner network consists of a single layer of size 1024, also with tanh activation. In total, this leads to a parameter count of around 79M. In addition to BPE labels the context-1 transducer also supports phoneme labels. The decoding options are identical to the full-context transducer. 3.5. Attention Encoder-Decoder Our AED system uses the same features and en- coder as our CTC system. For the decoder, we use a 6-layer Transformer with a dimension of 512. The model has 101M parameters and uses the same number of layers and hidden sizes as the 100M parameter model from the original Loquacious pub- lication. The model is trained using cross-entropy loss with label smoothing and two CTC auxiliary losses on the 4th and 8th encoder layer. Decoding is done without external LM using a custom imple- mentation of label-synchronous beam search. This is different from the original Loquacious publication, which used joint AED/CTC decoding. 3.6. Factored Hybrid Our diphone FH has an overall number of 75M parameters. Decoding is performed using time- synchronous beam search based on dynamic pro- gramming and lexical prefix trees. It is important to note that consistent use of pronunciation variants is only well-defined for this model, as it does not include a blank label. This is due to the fact that the context dependency modeled on the static de- coder structure, as done in the classic weighted finite state framework, is problematic when a blank label is introduced. In Section 4.5, we show the effect of adding pronunciation variants in decoding only. 4. Experimental Results 4.1. Baselines We first evaluate CTC, RNN-T and AED mod- els without the introduced lexicon and LMs for train.small and train.medium. Table 2 shows the results for each of the architectures with BPE labels and with greedy decoding or optimal beam search. Here, we do not include results of the original Lo- quacious publication, as they did not use any data augmentation, which is essential for good results on smaller datasets. See Section 4.6 for experiments on the effect of regularization techniques and a fair comparison to the original publication. We tested 128, 256, 512, and 1k labels for CTC and RNN-T and 1k, 2k, and 10k for AED. For CTC and RNN-T, 128 was always slightly better than larger sizes, while for AED 1k is optimal for train.small and 10k for train.medium. From the comparison of different ASR systems on the different test sets, it is clearly visible that it is important to have a task with various testing conditions and different training data scales. For systems trained on train.medium, the results on LibriSpeech and VoxPopuli are very homogeneous for AED and RNN-T. For Yodas and Commonvoice, there are larger differences. When switching from small to medium, doing beam search became more important for both RNN-T and AED on Yodas, but less relevant on LibriSpeech and VoxPopuli.
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] |
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 4
|
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[]
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2512.17915_page_5
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4.2. Count-based Language Model We use the 216k words vocabulary from Section 2.1 and the pruned 4-gram LM from Section 2.3 to fur- ther improve on the baseline results as shown in Table 3. As expected using a lexical tree-based search with a language model yields much better results for models trained on train.small. Surpris- ingly, just performing vocabulary restricted search without a language model already improved the BPE-based model as well. We see such improve- ments for all systems trained on train.small as well as for CTC on train.medium. But even the context- dependent transducer models still showed slight improvements on the train.medium. When adding the 4-gram LM, all models largely improve. We also tested for a shift in results when we optimize the LM-scale and prior scale for a CTC not on the combined dev set, but on each of the subsets. For CTC trained on train.small, we observed that differ- ent scales yield an improvement from 17.2 to 16.6 on the Yodas test set. Table 4 shows BPE-CTC ASR results using the four LMs, for which the statistics were presented in Table 1. Despite the difference in LM perplexities, we de do not see a substantial difference in the total Loquacious WER, but only for the Yodas test set. When adding the official LibriSpeech LM data8, we only see improvements on the LibriSpeech test set. The other sets remain the same, or in case of Yodas, even slightly degrade. Given that the LM with LibriSpeech data is 20 times larger (6GB instead of 300MB) than the 3-gram, it does not really provide much additional benefit. 4.3. Phoneme-Lexicon Using the CTC system, we compare the perfor- mance when replacing BPE labels with phonemes using the created pronunciation lexicon from Sec- tion 2.2. Table 5 shows the results of the phoneme- based CTC systems compared to the BPE labels. It can be seen that when training on train.small only the phonetic-based CTC system slightly outper- forms its BPE-based counterpart. On train.medium, the phonetic-based system variant is slightly worse compared to its BPE counterpart for all architec- tures. Nevertheless, the performance gap is small enough so that the phonetic-based systems can be used for scenarios where this brings advantage, as e.g. for applications where custom pronunciation lexicon entries are desired, or for using systems with more explicit alignment modeling such as FH. 4.4. Restricted Vocabulary To verify that it is actually necessary to expand the vocabulary and lexicon beyond what is given in 8https://www.openslr.org/11/ the CMUdict, we perform recognition with a lexicon and LM that was restricted to words in the CMUdict. Table 6 shows the results for the CTC model trained on train.small. There is a noticeable degradation, showing the justification to expanding the original CMUdict lexicon via G2P. 4.5. Pronunciation Variants The CTC pipeline does not support pronunciation variants, so we use the FH pipeline to check if pronunciation variants generated by the G2P pro- cess are relevant or not. The lexica are defined as described in Section 2.2, so one without and two with additional G2P generated pronunciation variants. We train two models, one without see- ing any pronunciation variants, and one without variants for words that are not in CMUdict. The recognition process either includes the lexicon that was used for training, or the augmented lexica with different probability masses. Table 7 shows that adding pronunciations during decoding helps, or does not lead to significant degradation. The ra- tio of words with G2P generated pronunciations in dev.all is only 0.6%, and only about 50% of those get a second pronunciation, so the potential of im- proving accuracy by adding more variants is rather limited. Moreover, the model trained on single pro- nunciations generally perform better. We believe this might be due to the additional alignment paths corresponding to the variants, which might affect the convergence of the model trained from scratch. 4.6. Regularization and Overfitting Behavior The original Loquacious publication reported only results without data augmentation, with the excep- tion of their largest system trained on train.large. We tested the effect of SpecAugment and speed perturbation, which are standard augmentations that were used for past LibriSpeech, TED-Lium and Switchboard systems. Table 8 shows the results for the AED and for the CTC system with 4-gram LM when disabling SpecAugment and speed perturba- tion. It is widely assumed that data augmentation is essential when working with rather small data sizes of a few hundred or thousand hours. We also ob- served substantial improvements for train.medium. 5. Further Analysis and Statistics In this section, we want to share some of the in- sights we gained while working with Loquacious, which are meant to show that it presents interesting challenges which are not present in more artificial datasets like LibriSpeech.
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https://arxiv.org/pdf/2512.17915
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2512.17915
| 5
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2512.17915_page_6
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Table 3: Recognition results for different BPE ASR systems to compare the effect of vocabulary restricted search and the addition of the pruned 4-gram LM. Abbreviations for test sets: Commonvoice (CV), LibriSpeech (LS), VoxPopuli (VP), Yodas (YD). Data Architecture Vocab LM WER [%] Loquacious CV LS VP YD dev test test train.small 250h CTC open no 17.2 18.6 30.1 16.7 12.5 21.1 closed 16.2 17.3 28.2 15.4 11.7 19.9 yes 12.6 13.8 21.8 11.6 10.2 17.2 RNN-T open no 14.9 15.9 26.3 13.9 10.9 17.6 closed 14.6 15.5 25.7 13.6 10.7 17.2 yes 13.1 14.1 22.9 11.9 10.1 16.5 mRNN-T open no 15.4 16.5 27.0 14.6 11.2 18.6 closed 14.7 15.7 25.6 13.8 10.8 18.2 yes 12.2 13.4 21.5 11.1 9.7 16.7 train.medium 2500h CTC open no 10.6 11.5 19.3 9.0 8.1 15.4 closed 10.0 10.8 18.2 8.4 7.7 14.4 yes 8.4 9.2 14.6 6.8 7.4 12.4 RNN-T open no 8.5 9.3 15.4 7.1 7.0 12.6 closed 8.4 9.2 15.3 7.0 6.9 12.5 yes 8.0 8.8 14.0 6.4 6.8 14.0 mRNN-T open no 9.1 10.2 16.3 7.3 7.3 18.8 closed 8.9 10.0 15.8 7.1 7.2 18.7 yes 8.0 8.8 14.3 6.4 7.1 12.0 Table 4: Recognition results for different n-gram LMs with a BPE-CTC on train.medium. LM WER[%] Loquacious LS YD dev test test 3-gram pruned 8.4 9.2 6.8 12.8 4-gram pruned 8.4 9.2 6.8 12.4 + LibriSpeech 8.3 9.1 6.5 12.9 4-gram unpruned 8.3 9.1 6.8 12.0 5.1. Detailed Word Error Rates Tables 9 and 10 show the detailed WERs for the LibriSpeech and Yodas dev sets for different mod- els and label units trained on the train.small and train.medium sets, respectively. For AED, we re- port results for different vocabulary sizes because we observed much worse performance with the 10K vocabulary on train.small. For LibriSpeech, we observe that for a given model the amount of deletions and insertions lie in a similar range. Fur- thermore, these numbers are also similar across models. For Yodas however, we observe differ- ent error patterns for different configurations. For train.small (Table 9), all models have more dele- tions than insertions. For train.medium (Table 10), AED and RNN-T have more insertions than dele- tions, while CTC has more deletions than inser- tions. In order to better understand the error patterns, we manually compared the model outputs to the references. Regarding the insertion behavior of our models, our experience on LibriSpeech shows that they sometimes transcribe single words as multiple words (e.g.“await” →“a weight”) or, in case of AED, attend to the same audio segment multiple times, resulting in repeated words. On the Yodas dev set, we additionally observe “oscillations” (Frieske and Shi, 2024), i.e. cases where most of the model out- put is correct, but with the addition of a repeating n-gram. Most frequently, models insert a name at the beginning of an utterance (e.g. “would involve...” →“Matheus Aaron would involve...”), which is re- lated to the name appearing in training transcripts without being present in the audio (see Section 5.2). Interestingly, we observe this issue for all models, except for a variant of FH for which we used chun- ked Viterbi training with a fixed target alignment. Concerning the case of high deletions, we ob- serve that all models omit large sections of some utterances in varying degrees. This behavior is most extreme in case of the CTC models, where almost whole utterances are sometimes omitted. Interestingly, the sequences where this happens are mostly disjoint between the AED model on the one side and the CTC and RNN-T models on the other side. Upon manual inspection, the affected utterances do not seem to be less intelligible than
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https://arxiv.org/pdf/2512.17915
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2512.17915
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2512.17915_page_7
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Table 5: Recognition results for different ASR systems comparing BPE to phoneme performance. Results of FH are excluding speed perturbation. All results include decoding with the pruned 4-gram LM. Abbrevi- ations for test sets: Commonvoice (CV), LibriSpeech (LS), VoxPopuli (VP), Yodas (YD). Data Architecture Label WER [%] Loquacious CV LS VP YD dev test test train.small 250h CTC BPE 12.6 13.8 21.8 11.6 10.2 17.2 Phon 12.3 13.4 21.0 11.2 10.3 15.9 mRNN-T BPE 12.2 13.4 21.5 11.1 9.7 16.7 Phon 12.5 13.6 21.6 11.2 10.4 16.6 FH Phon 12.5 13.5 22.0 11.2 10.2 15.4 train.medium 2500h CTC BPE 8.4 9.2 14.6 6.8 7.4 12.4 Phon 8.8 9.8 15.2 7.4 7.8 13.7 mRNN-T BPE 8.0 8.8 14.3 6.4 7.1 12.0 Phon 8.5 9.2 14.5 6.8 7.7 12.1 FH Phon 9.0 9.7 15.2 7.4 7.9 13.0 Table 6: Recognition results and OOV rates with the BPE-CTC using a restricted lexicon on train.small. Test-Set Lexicon CMUdict Full OOV[%] WER[%] OOV[%] WER[%] Loq. 1.85 14.5 0.47 13.8 CV 2.53 22.9 0.99 21.8 LS 2.00 12.4 0.50 11.6 VP 1.33 10.6 0.11 10.2 YD 1.46 17.3 0.29 17.2 Table 7: Absolute errors for two FH models trained on train.small using different lexica. We show the effect of addition of pronunciation variants during recognition. Pronunciation Variant CV LS VP YD Train Recog None None 5257 5449 4340 867 60% mass 5234 5439 4347 868 80% mass 5190 5426 4336 898 CMUdict CMUdict 5325 5567 4403 882 60% mass 5324 5557 4403 875 80% mass 5305 5551 4391 871 other utterances. Furthermore, all models have issues when speakers are spelling words letter by letter, in which case however the AED model suc- ceeds more often than the other models. Lastly, it is interesting to observe that with in- creased training data the “weaknesses” of the AED and phoneme CTC models, i.e. insertions and deletions, become more pronounced on Yodas but are halved for LibriSpeech (compare Table 9 and Table 10). For all models, except the BPE CTC, Table 8: Recognition results in WER (%) on the train.small subset for removing SpecAugment and speed perturbation. Original refers to the 100M system in the original Loquacious publication. Architecture Spec- Aug. Speed Pert. Loquacious dev test Original no no 22.3 23.8 AED 21.4 23.1 yes 15.1 16.6 yes 14.7 16.0 CTC + LM no no 19.1 20.6 yes 12.9 13.9 yes 12.6 13.8 the main driver for the reduction in WER on Yodas when switching to train.medium is the reduction in substitutions. For the BPE CTC, we even observe a small overall degradation with more training data, for which we don’t have an explanation yet. 5.2. Challenges of the Dataset As noted in the original Loquacious publication, the dataset contains both clean, read speech, as well as more noisy, spontaneous speech, and every- thing in between. Even though the authors em- ployed measures like language identification to fil- ter out erroneous segments, there are remaining aspects that make Loquacious more challenging compared to other academic tasks. In addition to the training data having transcriptions that do not match the audio in content or language, five of the Commonvoice test set utterances contained no speech even though they had non-empty tran- scriptions. We manually pickled 30 examples of train.small that a CTC model transcribed as empty.
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] |
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 7
|
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2512.17915_page_8
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Table 9: Detailed WERs in terms of substitutions, insertions, and deletions on the LibriSpeech and Yodas dev sets for different models trained on train.small. CTC, mRNN-T, and FH results are with 4-gram LM. In case of phonemes, we use end-of-word augmentation. · Architecture Labels WER [%] LibriSpeech Yodas Tokens Number Sub Del Ins Σ Sub Del Ins Σ AED BPE 1K 10.9 1.1 1.7 13.7 9.9 4.5 3.9 18.3 CTC 128 8.9 1.3 1.1 11.3 8.3 6.4 2.1 16.8 Phonemes 80 8.4 1.0 1.3 10.6 8.1 5.1 2.4 15.6 RNN-T BPE 128 11.3 1.3 1.3 13.9 9.6 5.4 3.5 18.5 mRNN-T 8.4 1.3 1.0 10.7 7.0 6.7 2.2 15.9 Phonemes 80 8.3 1.3 1.2 10.8 7.9 6.1 2.3 16.3 FH 8.4 1.6 1.2 11.2 8.2 5.1 2.5 15.8 Table 10: Detailed WERs on the LibriSpeech and Yodas dev sets for different models trained on train.medium. CTC results are with 4-gram LM. In case of phonemes, we use end-of-word augmentation. Architecture Labels WER [%] LibriSpeech Yodas Tokens Number Sub Del Ins Σ Sub Del Ins Σ AED BPE 10K 5.6 0.5 0.8 6.9 5.4 3.0 4.8 13.2 CTC 128 7.6 0.7 0.7 9.0 8.1 6.6 2.4 17.1 Phonemes 80 5.7 0.6 0.8 7.0 5.8 6.0 2.3 14.0 RNN-T BPE 128 5.7 0.6 0.7 7.0 5.5 3.3 3.8 12.6 We found that all of them contained speech, but 27 of them did not match the provided transcription at all and 3 turned out to be the Indonesian transla- tions of the English speech. Lastly, we found that some transcripts in the training sets contain names at the beginning of the utterance that are not spo- ken. For example, the name “Matheus Aaron” is prepended to 10 utterances in train.small and 74 ut- terances in train.medium. We manually confirmed the absence of this name within the audio of the 10 utterances in train.small. Such aspects make the Loquacious dataset more realistic to work with, while still being well defined and normalized. 6. Future Work Given the short time since the release of the Loqua- cious dataset, we focused only on the 250 hours and 2.5k hours subsets. This is fine in order to investigate the Loquacious dataset as replacement for common smaller academic tasks such as Lib- riSpeech or TED-Lium. Still, it is important to ex- pand such studies to the full 25k hours dataset. For this task, in order to investigate improvements with LM integration, much larger text corpora are needed which have to be normalized and standard- ized to fit the Loquacious task. Also, the current systems might improve with further optimization and tuning specifically for the Loquacious task. 7. Conclusion In this work, we presented additional resources related to ASR training and evaluation with the Lo- quacious dataset. The following resources are pub- lished online under a permissive license: • Vocabulary file containing 216k words • Lexicon files containing 223k (no G2P variants) and 280k (with G2P variants) pronunciations • Sequitur G2P model file • A set of different ARPA count-based LMs trained with KenLM: – pruned 3-gram ARPA LM – pruned 4-gram ARPA LM – un-pruned 4-gram ARPA LM The creation of such resources allowed us to conduct experiments with a large variety of differ- ent settings related to ASR architectures, decoding methods and label types. We found that on both the 250 hours train.small and 2.5k hours train.medium subset the usage of a simple count-based LM sub- stantially improves recognition results. More sur- prisingly, even just using a word vocabulary con- strained search would yield improvements. We compared BPE to phoneme representations, show- ing that phonemes only outperform BPE for a CTC model trained on the 250 hours subset. Still, the phoneme-based systems had a comparable perfor-
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] |
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 8
|
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mance, and for certain research or applications it might be beneficial to also explore such systems. We discussed some aspects that make Loquacious more interesting for research compared to earlier academic datasets or other alternatives. Finally, we presented first results with multiple architectures that can be used as reference for reasonable ASR performances on the train.small and train.medium sub-tasks. 8. Limitations The biggest limitation of this work is, that it excludes the 25k hours train.large subset of the Loquacious dataset. Working on such data needs a much larger amount of technical preparation, model sizes and training times, which were out of the scope for this work. In particular, we wanted to look at Loqua- cious as a replacement for smaller academic tasks, rather covering a variety of conditions instead of aiming for a single best result. What is excluded regarding the variety are further experiments re- garding the model size, as all models are in a sim- ilar range of parameters between roughly 80M to 100M. While for the BPE-models the BPE sizes are approximately tuned to be optimal, there are some edge cases such as character representa- tions which we did not consider yet. We were not able to consistently publish all the training recipes, which is planned for a further publication. Never- theless, the continuous progress of our Loquacious pipelines can be found in our public pipeline repos- itory9. 9. Acknowledgements This work was partially supported by NeuroSys, which as part of the initiative “Clusters4Future” is funded by the Federal Ministry of Education and Research BMBF (funding IDs 03ZU2106DA and 03ZU2106DD), and by the project RESCALE within the program AI Lighthouse Projects for the Environment, Climate, Nature and Resources funded by the Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Con- sumer Protection (BMUV), funding ID: 67KI32006A. The authors gratefully acknowledge the comput- ing time provided to them at the NHR Center NHR4CES at RWTH Aachen University (project number: p0023999). This is funded by the Federal Ministry of Education and Research, and the state governments participating on the basis of the reso- lutions of the GWK for national high performance computing at universities. We thank Titouan Par- collet for allowing us to host our resources as part 9https://github.com/rwth-i6/i6_ experiments of the official Loquacious release, and thank Mattia Di Gangi, Florian Lux and Moritz Gunz for critical proofreading. 10. Bibliographical References Vladimir Bataev, Andrei Andrusenko, Lilit Grigo- ryan, Aleksandr Laptev, Vitaly Lavrukhin, and Boris Ginsburg. 2025. NGPU-LM: GPU- Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding. In Proc. Interspeech, pages 644–648. Maximilian Bisani and Hermann Ney. 2008. Joint- sequence models for grapheme-to-phoneme conversion. Speech Communication, 50(5):434– 451. Hervé A. Bourlard and Nelson Morgan. 1993. Con- nectionist Speech Recognition: a Hybrid Ap- proach. Kluwer Academic Publishers, Norwell, MA. William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals. 2016. Listen, attend and spell: A neu- ral network for large vocabulary conversational speech recognition. In Proc. IEEE ICASSP, pages 4960–4964. Rita Frieske and Bertram E. Shi. 2024. Hallu- cinations in neural automatic speech recogni- tion: Identifying errors and hallucinatory models. Preprint arXiv: 2401.01572. J.J. Godfrey, E.C. Holliman, and J. McDaniel. 1992. Switchboard: telephone speech corpus for re- search and development. In Proc. IEEE ICASSP, volume 1, pages 517–520 vol.1. Alex Graves. 2012. Sequence transduction with recurrent neural networks. In RL 2012 - ICML Workshop on Representation Learning. Alex Graves, Santiago Fernández, Faustino Gomez, and Jürgen Schmidhuber. 2006. Con- nectionist temporal classification: labelling un- segmented sequence data with recurrent neural networks. ICML ’06, page 369–376, New York, NY, USA. Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang. 2020. Conformer: Convolution- augmented transformer for speech recognition. In Proc. Interspeech, pages 5036–5040.
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] |
https://arxiv.org/pdf/2512.17915
|
2512.17915
| 9
|
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2512.17915_page_10
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https://arxiv.org/pdf/2512.17915
|
2512.17915
| 10
|
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[]
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2512.22106_page_1
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Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks Zubair Shah College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar [email protected] Noaman Khan College of Science and Engineering Hamad Bin Khalifa University Doha, Qatar [email protected] Abstract Neural network pruning is widely used to reduce model size and computational cost. Yet, most existing methods treat sparsity as an externally imposed constraint, enforced through heuristic importance scores or training-time regularization. In this work, we propose a fundamentally different perspective: pruning as an equilibrium outcome of strategic interaction among model components. We model parameter groups such as weights, neurons, or filters as players in a continuous non-cooperative game, where each player selects its level of participation in the network to balance contribution against redundancy and competition. Within this formulation, sparsity emerges naturally when continued participation becomes a dominated strategy at equilibrium. We analyze the resulting game and show that dominated players collapse to zero participation under mild conditions, providing a principled explanation for pruning behavior. Building on this insight, we derive a simple equilibrium-driven pruning algorithm that jointly updates network parameters and participation variables without relying on explicit importance scores. This work focuses on establishing a principled formulation and empirical validation of pruning as an equilibrium phenomenon, rather than exhaustive architectural or large-scale benchmarking. Experiments on standard benchmarks demonstrate that the proposed approach achieves competitive sparsity– accuracy trade-offs while offering an interpretable, theory-grounded alternative to existing pruning methods. 1 Introduction Neural network pruning is a central technique for reducing model size, computational cost, and energy consumption without retraining from scratch. Over the past decade, a wide range of pruning methods have been proposed, including magnitude-based thresholding, sensitivity and saliency metrics, and lottery-ticket style rewinding. Despite their empirical success, these methods share a common conceptual assumption: pruning is treated as a centralized, post-hoc decision, applied externally to a trained model using heuristics that rank parameters by importance. This prevailing view implicitly assumes that sparsity is something that must be imposed on a network. Parameters are evaluated, scored, and removed by an external criterion, typically based on magnitude, gradients, or training dynamics. While effective in practice, this perspective offers limited insight into a more fundamental question: why does sparsity emerge in overparameterized networks at all? In particular, existing approaches do not model the interactions among parameters that lead some components to become redundant while others remain essential. In this work, we argue that pruning is more naturally understood as the outcome of strategic interaction among model components competing for limited representational resources. During training, parameters do not contribute independently; instead, they interact through shared gradients, overlapping activations, and redundant representations. Some components provide unique and arXiv:2512.22106v1 [cs.AI] 26 Dec 2025
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 1
|
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2512.22106_page_2
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indispensable contributions, while others become increasingly redundant as training progresses. From this perspective, sparsity is not an externally enforced constraint, but an emergent property of competition and dominance among parameters. Motivated by this observation, we propose a game-theoretic formulation of neural network pruning. We model parameter groups such as weights, neurons, or filters as players in a game whose strategies determine their level of participation in the network. Each player receives a payoff that balances its contribution to the training objective against the cost of redundancy and competition with other players. Pruning arises naturally when a player’s optimal strategy collapses to zero at equilibrium, indicating that continued participation is no longer beneficial. Contributions. The main contributions of this paper are: • We introduce a game-theoretic formulation of neural network pruning, modeling parameter groups as strategic players. • We show that sparsity emerges naturally as a stable equilibrium of the proposed game. • We derive a simple equilibrium-driven pruning algorithm grounded in this theoretical frame- work. • We empirically demonstrate that the proposed approach achieves competitive sparsity-accuracy trade-offs while providing a principled explanation for pruning behavior. 2 Related Work Early pruning methods focused on estimating the sensitivity of the loss function to parameter removal. Optimal Brain Damage (OBD) [1] and Optimal Brain Surgeon (OBS) [2] introduced second-order Taylor expansions to quantify the impact of pruning individual weights. While theoretically grounded, these methods rely on Hessian computations and do not scale efficiently to modern deep networks. 2.1 Magnitude and Regularization-Based Pruning Magnitude-based pruning removes parameters with small absolute values, often iteratively combined with fine-tuning [3]. Regularization-based approaches relax the intractable ℓ0 minimization problem using ℓ1 or ℓ2 penalties and soft-thresholding [4], while stochastic ℓ0 techniques enable learning sparse structures directly [5]. Relevance-based methods [6] assign importance scores to neurons and iteratively remove low-scoring units. 2.2 Structured and Channel Pruning Structured pruning removes entire filters, channels, or neurons to enable hardware-friendly acceler- ation [7]. Filter-level pruning methods [8] prune convolutional filters by ranking their importance via ℓ1-norm or gradient-based metrics. Soft filter pruning [9] introduces smooth masking to enable differentiable channel selection during training. 2.3 Pruning During Training and Dynamic Sparse Optimization Recent research focuses on pruning jointly with weight optimization, enabling sparse networks from scratch without dense pretraining. Dynamic sparse training [10] continuously removes and regrows connections throughout training, maintaining sparsity while adapting topology. Lottery ticket hypothesis studies [11–13] demonstrate that sparse subnetworks can be identified early in training and match dense performance when rewound to initial conditions. 2.4 Pruning Large Language Models Pruning LLMs presents unique challenges due to scale and parameter sensitivity. SparseGPT [14] applies layer-wise pruning with approximate reconstruction to large transformers. WANDA [15] introduces weight-magnitude and activation-based metrics optimized for LLM pruning. LoSparse [16] combines low-rank and sparse approximations to compress large models efficiently.
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 2
|
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2512.22106_page_3
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2.5 Game-Theoretic Perspectives in Learning Game-theoretic concepts have been applied to model adversarial learning [17], multi-agent reinforce- ment learning [18, 19], distributed optimization [20], and federated learning [21]. However, their application to pruning as an equilibrium phenomenon remains unexplored. 2.6 Positioning of This Work Our approach differs fundamentally from previous pruning methods by modeling pruning as an equilibrium process driven by strategic interactions, rather than as an optimization problem with externally imposed sparsity constraints. We demonstrate that sparsity can be reinterpreted as a natural outcome of competition among parameter groups, offering a unifying framework for understanding existing heuristics while guiding the design of new pruning algorithms. 3 Problem Setup and Preliminaries We consider a supervised learning setting with input–output pairs (x, y), a neural network f(x; θ), and a training objective defined by a loss function L(θ). The parameter vector θ is assumed to be overparameterized, containing redundancy that can be removed without significantly degrading performance. Rather than treating individual scalar weights as atomic units, we partition the parameter vector into N groups: θ = {θ1, θ2, . . . , θN}, (1) where each group θi may correspond to a single weight, a neuron, a convolutional filter, or any other logically coherent subset of parameters. This abstraction allows the framework to capture different granularities of pruning. 3.1 Participation Variables To model the degree to which each parameter group participates in the network, we associate with every group θi a participation variable si ∈[0, 1]. (2) The effective parameters used by the network are given by ˜θi = si · θi, (3) and the forward computation becomes f(x; θ, s) = f(x; {siθi}N i=1), (4) where s = (s1, . . . , sN) denotes the vector of participation variables. The interpretation of si is intuitive: values close to one indicate full participation of the corre- sponding parameter group, while values approaching zero indicate diminishing influence. In the limit si →0, the group θi effectively drops out of the model. Pruning is thus modeled as the collapse of participation variables rather than an explicit hard removal operation. 3.2 Training Objective with Participation Given the participation variables, the training objective can be written as L(θ, s) = E(x,y)[ℓ(f(x; θ, s), y)], (5) where ℓ(·) denotes the per-sample loss. For fixed participation s, optimizing θ corresponds to standard network training under a reweighted parameterization. Conversely, adjusting s modulates the influence of parameter groups on the loss landscape. Importantly, the participation variables do not merely act as static gates. Because the loss depends jointly on all si, changes in one group’s participation affect the marginal contribution of others. This coupling induces competition and redundancy among parameter groups, which forms the basis for the strategic interactions modeled in the next section.
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 3
|
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2512.22106_page_4
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3.3 From Optimization to Interaction Traditional pruning methods implicitly evaluate parameter groups in isolation by assigning importance scores derived from magnitude, gradients, or training trajectories. In contrast, our formulation emphasizes that the utility of a parameter group depends on the collective configuration of the network. A group that is useful in one context may become redundant when other groups provide overlapping functionality. This observation motivates a shift from viewing pruning as a centralized optimization problem to viewing it as an interaction among parameter groups. By interpreting each group as an agent whose participation level affects and is in turn affected by others, we establish a natural bridge to a game-theoretic formulation. In the following section, we formalize this interaction by defining players, strategies, and payoffs, and show how sparse configurations arise as equilibrium outcomes. 4 Pruning as a Strategic Game We now formalize the interaction among parameter groups introduced in Section 3 as a strategic game. This formulation makes explicit how pruning arises as an equilibrium phenomenon rather than as an externally imposed operation. 4.1 Players and Strategies We model each parameter group θi as a player in a game. The set of players is given by N = {1, 2, . . . , N}, (6) where each player controls its own participation variable si ∈[0, 1], as defined in Section 3. The strategy of player i is its choice of participation level si, which determines the extent to which θi contributes to the network’s computation. The joint strategy profile is denoted by s = (s1, s2, . . . , sN) ∈[0, 1]N. (7) This continuous strategy space avoids hard combinatorial decisions and allows pruning to emerge smoothly as a limiting behavior when strategies collapse toward zero. A player is considered pruned when its equilibrium strategy satisfies si ≈0. 4.2 Utility Functions Each player seeks to maximize a utility function that captures the trade-off between useful contribution to the learning objective and costs arising from redundancy and competition. We define the utility of player i as Ui(si, s−i) = Bi(si, s−i) −Ci(si, s−i), (8) where s−i denotes the strategies of all players except i. Benefit Term The benefit term Bi(·) quantifies the marginal contribution of player i to the overall training objective. A simple linearization yields Bi(si, s−i) = α · si · ⟨∇θiL(θ, s), θi⟩, (9) where α > 0 is a scaling parameter and ∇θiL denotes the gradient of the loss with respect to the parameters in group i. The gradient inner product captures how effectively the parameter group reduces the training loss. Large gradients indicate that changes in θi significantly affect the objective, motivating higher participation. When gradients are small or aligned poorly with existing parameter values, the benefit diminishes, encouraging the player to reduce participation. Cost Term The cost term Ci(·) penalizes redundancy and competition. We consider a general quadratic cost structure: Ci(si, s−i) = β∥θi∥2 2s2 i + γ|si| + ηsi X j̸=i sj⟨θi, θj⟩, (10)
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https://arxiv.org/pdf/2512.22106
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2512.22106
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2512.22106_page_5
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where β, γ, η ≥0 are hyperparameters controlling the strength of different cost components. The first term penalizes participation scaled by the ℓ2-norm of the parameter group, discouraging large magnitudes from dominating. The second term imposes an ℓ1-style sparsity cost, promoting exact zeros at equilibrium. The third term captures direct competition: players whose parameters are highly correlated impose mutual costs on each other, incentivizing one to drop out. 4.3 Nash Equilibrium A strategy profile s∗= (s∗ 1, . . . , s∗ N) is a Nash equilibrium if no player can improve its utility by unilaterally changing its strategy: Ui(s∗ i , s∗ −i) ≥Ui(si, s∗ −i) ∀i ∈N, ∀si ∈[0, 1]. (11) At equilibrium, each player is playing a best response to the strategies of others. If s∗ i = 0, we say that player i is pruned at equilibrium, meaning that zero participation is its optimal strategy given the configuration of other players. 4.4 Dominated Strategies and Sparsity A key insight of the game-theoretic formulation is that pruning corresponds to dominated strategies. A player i has a dominated strategy if there exists another strategy (in this case, si = 0) that yields strictly higher utility regardless of what other players do: Ui(0, s−i) > Ui(si, s−i) ∀si > 0, ∀s−i. (12) When costs outweigh benefits, zero participation becomes dominant, and the player is pruned. Conversely, players whose benefits exceed costs remain active at equilibrium. The game-theoretic framework thus provides a formal explanation for why some parameters survive pruning while others do not: survival depends on whether a parameter group can achieve positive utility in the competitive environment defined by other players. 5 Theoretical Analysis We now analyze the properties of the proposed game and establish conditions under which sparse equilibria emerge. 5.1 Best Response Dynamics For a given player i, the best response to the strategies of other players is the participation level s∗ i that maximizes Ui(si, s−i). Taking the derivative of the utility function and setting it to zero yields the first-order condition: ∂Ui ∂si = α⟨∇θiL, θi⟩−2β∥θi∥2 2si −γ sign(si) −η X j̸=i sj⟨θi, θj⟩= 0. (13) The L1 penalty introduces a non-differentiable point at si = 0, resulting in a soft-thresholding effect. For si > 0, the solution satisfies: s∗ i = α⟨∇θiL, θi⟩−γ −η P j̸=i sj⟨θi, θj⟩ 2β∥θi∥2 2 . (14) If the numerator is negative or zero, the optimal strategy is s∗ i = 0, indicating that participation is not beneficial. This provides a clear criterion for pruning: players whose gradient contribution is insufficient to overcome costs will collapse to zero at equilibrium. 5.2 Conditions for Sparse Equilibria To ensure that some players are pruned at equilibrium, we require that costs dominate benefits for a subset of players. Formally, a player i will be pruned if: α⟨∇θiL, θi⟩< γ + η X j̸=i sj⟨θi, θj⟩. (15)
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 5
|
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2512.22106_page_6
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This condition has an intuitive interpretation: pruning occurs when the marginal contribution of a parameter group (left-hand side) is outweighed by sparsity costs and competition from other players (right-hand side). For networks with redundancy, many parameter groups will satisfy this condition, leading to sparse equilibria. Conversely, indispensable players whose contributions remain large throughout training will maintain positive participation. 5.3 Stability of Equilibria We say that an equilibrium s∗is stable if small perturbations decay over time under best-response dynamics. Analyzing the Jacobian of the best-response mapping shows that the game exhibits contraction properties when the competition term η is not too large, ensuring convergence to a unique equilibrium. When multiple equilibria exist, the selection among them depends on initialization and the trajectory of training. Empirically, we observe that starting from full participation (s = 1) leads to equilibria where only redundant players are pruned, preserving network performance. 5.4 Interpretation of Pruning Heuristics The equilibrium framework provides a unifying explanation for several existing pruning heuristics: • Magnitude-based pruning corresponds to the case where benefits are proportional to parame- ter norms, and small-magnitude parameters have dominated strategies. • Gradient-based pruning aligns with the benefit term’s dependence on ⟨∇θiL, θi⟩, favoring parameters with large gradient contributions. • Redundancy-aware pruning emerges from the competition term, which penalizes correlated parameters. By making these connections explicit, the game-theoretic formulation bridges existing pruning methods and offers a principled foundation for designing new algorithms. 6 Equilibrium-Driven Pruning Algorithm Building on the theoretical analysis, we now describe a simple algorithm for training sparse networks by allowing participation variables to evolve toward equilibrium. 6.1 Joint Optimization of Parameters and Participation Rather than separating training and pruning into distinct phases, we propose to jointly optimize the network parameters θ and the participation variables s. The algorithm alternates between: 1. Parameter update: Perform gradient descent on θ with respect to the loss L(θ, s): θ ←θ −ηθ∇θL(θ, s). (16) 2. Participation update: Perform projected gradient ascent on the utilities Ui to move participa- tion variables toward their best responses: si ←Proj[0,1] (si + ηs∇siUi(si, s−i)) , (17) where Proj[0,1](·) denotes projection onto the interval [0, 1]. 6.2 Gradient of Utility The gradient of the utility with respect to participation is: ∇siUi = α⟨∇θiL, θi⟩−2β∥θi∥2 2si −γ sign(si) −η X j̸=i sj⟨θi, θj⟩. (18)
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 6
|
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2512.22106_page_7
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Algorithm 1 Equilibrium-Driven Pruning 1: Input: training data, initial parameters θ, initial participation s = 1 2: Output: pruned parameters ˜θ 3: Initialize participation variables si = 1 for all players i 4: for training iterations t = 1, . . . , T do 5: Update θ using gradient descent on L(θ, s) 6: Update s using projected gradient ascent on utilities Ui 7: end for 8: Prune all parameter groups with si < ε 9: Return pruned model At each iteration, this gradient is computed and used to update the participation variables. Players with positive gradients increase participation, while those with negative gradients decrease participation, eventually collapsing to zero if costs consistently dominate benefits. We initialize all participation variables to si = 1, representing full participation at the start of training. Hyperparameters α, β, γ, η control the trade-off between benefits and costs, and learning rates ηθ, ηs control the speed of convergence. In practice, we set α = 1 and tune β, γ to achieve desired sparsity levels. The competition term η can be set to zero for simplicity, as redundancy costs alone are sufficient to induce pruning. After training, we prune all parameter groups with si < ε for some small threshold ε > 0 (e.g., ε = 0.01). This threshold accounts for numerical precision and ensures that near-zero participation values are treated as exact zeros. The overall procedure is summarized in Algorithm 1. 6.3 Discussion The proposed algorithm is simple by design. It does not introduce complex solvers, discrete op- timization steps, or specialized pruning schedules. Instead, pruning emerges as a by-product of equilibrium-seeking dynamics that suppress dominated strategies. This simplicity is intentional. The goal of this paper is not to engineer the most aggressive pruning scheme, but to demonstrate that a game-theoretic formulation leads naturally to a practical and interpretable pruning algorithm. More sophisticated dynamics, alternative utility specifications, and structured pruning variants are left for future work. 7 Experimental Settings 7.1 Dataset and Model Architecture We evaluate the proposed equilibrium-driven pruning approach on the MNIST handwritten digit dataset, a standard benchmark for controlled analysis of learning dynamics and sparsity behavior. MNIST consists of 60,000 training samples and 10,000 test samples of grayscale images with resolution 28 × 28. All images are flattened into 784-dimensional input vectors. MNIST is intentionally chosen for this initial study to allow clear inspection of participation dynamics and equilibrium behavior without confounding effects from deep architectures or complex data augmentation. We use a multi-layer perceptron (MLP) with two hidden layers: • Input layer: 784 features • Hidden layer 1: 512 neurons (Participating Linear) • Hidden layer 2: 256 neurons (Participating Linear) • Output layer: 10 neurons (standard linear layer) The model contains 536,586 trainable weight parameters and 768 participation variables corre- sponding to neurons in the two hidden layers. Participation variables are initialized to one, represent- ing full participation at the start of training. Participation variables are neuron-level scalar gates
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https://arxiv.org/pdf/2512.22106
|
2512.22106
| 7
|
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2512.22106_page_8
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learned jointly with network parameters, controlling the effective contribution of each neuron during training. 7.2 Training and Pruning Procedure Models are trained for 20 epochs with batch size 128. Network weights are optimized using cross- entropy loss, while participation variables are optimized jointly using equilibrium-driven updates. Participation values are constrained to [0, 1] via projection after each update. Neurons with final participation s < 0.01 are considered pruned. Initial experiments with mild cost penalties failed to induce pruning, motivating a set of more aggressive configurations combining L1 and L2 penalties, as detailed in Section 7.3. 7.3 Hyperparameter Configurations Initial experiments with mild cost penalties (β ∈[10−4, 5 × 10−3]) resulted in no neuron collapse, indicating that insufficient competition does not lead to dominated strategies. To study equilibrium- induced sparsity, we therefore evaluate five increasingly aggressive configurations combining L1 and L2 penalties: Table 1: Hyperparameter Configurations Configuration α β (L2) γ (L1) lrs Very High Beta 1.0 0.1 0.0 0.001 Extreme Beta 1.0 0.5 0.0 0.001 L1 Sparsity Strong 1.0 0.001 0.1 0.001 L1+L2 Combined 1.0 0.05 0.05 0.001 These configurations allow us to examine how equilibrium behavior transitions from dense to sparse regimes. 8 Results The following results focus on pruning dynamics, final sparsity patterns, and accuracy–sparsity trade-offs, with an emphasis on validating the equilibrium interpretation proposed in this paper. 8.1 Training Dynamics and Emergent Sparsity Figure 1 illustrates the evolution of test accuracy, sparsity, and mean participation across training for all configurations. Several consistent patterns emerge. First, configurations with insufficient cost pressure (e.g., Very High Beta) maintain high accuracy but exhibit no sparsity. Participation values decrease slightly but stabilize at small positive levels, indicating that zero participation is not a dominated strategy in this regime. Second, configurations with stronger penalties (Extreme Beta, L1 Sparsity Strong, and L1+L2 Combined) show a clear transition phase in which participation values collapse rapidly for a large subset of neurons. This collapse is smooth and occurs during training, rather than at a discrete pruning step, supporting the interpretation of pruning as an emergent equilibrium phenomenon. Finally, balanced configurations combining L1 and L2 costs (e.g L1+L2 Combined) achieve high sparsity while preserving accuracy. The smooth decrease in mean participation suggests a gradual equilibration process rather than abrupt thresholding. 8.2 Equilibrium Participation Distributions Figure 2 shows histograms of final participation values for each configuration, with the pruning threshold ε = 0.01 marked by a red dashed line. Successful pruning configurations exhibit bimodal participation distributions, with values con- centrated near zero or near one. This bimodality indicates that the equilibrium dynamics lead to near-binary decisions, despite the continuous strategy space. Neurons are either fully retained or effectively eliminated, rather than remaining in ambiguous intermediate states.
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 8
|
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[]
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2512.22106_page_9
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Figure 1: Training dynamics of equilibrium-driven pruning under different utility configurations. The four-panel visualization shows the evolution of test accuracy, sparsity, mean participation value, and number of active neurons over training epochs. Configurations with insufficient cost pressure converge to dense equilibria, while stronger L1 and combined L1+L2 penalties induce rapid collapse of dominated participation strategies. This bimodal structure is a hallmark of equilibrium behavior: neurons either commit fully to participation or drop out entirely. Intermediate participation values are unstable, as they do not correspond to best responses. This observation validates the interpretation of pruning as an equilibrium phenomenon driven by strategic interactions. In contrast, non-pruning configurations show unimodal distributions centered at small but non- zero participation values, consistent with dense equilibria predicted by the theory when costs do not dominate benefits. 8.3 Accuracy–Sparsity Trade-off Table 2 reports test accuracy and sparsity for each configuration. Table 2: Test accuracy with different sparsity configurations. Configuration Test Accuracy Sparsity Neurons Kept Very High Beta 96.64% 0.00% 100.00% Extreme Beta 91.15% 95.18% 4.82% L1 Sparsity Strong 89.57% 98.31% 1.69% L1+L2 Combined 91.54% 98.05% 1.95% Notably, the L1+L2 Combined configuration retains less than 2% of neurons while maintaining over 91% test accuracy. These results demonstrate that extreme redundancy exists in the original
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 9
|
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[]
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2512.22106_page_10
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Figure 2: Distribution of neuron participation values at convergence. Histograms of final participation values for each configuration, with the pruning threshold ε = 0.01 shown as a red dashed line. Successful pruning configurations exhibit bimodal distributions with mass concentrated near zero and one, indicating near-binary equilibrium decisions despite a continuous strategy space. Dense configurations show unimodal distributions centered away from zero. network and that equilibrium dynamics can identify and remove it without explicit importance scoring. 9 Discussion 9.1 Comparison with Magnitude-Based Pruning Traditional magnitude-based pruning relies on heuristic importance scores and typically requires multi-stage pipelines (train →prune →fine-tune). In contrast, our approach integrates pruning directly into the training objective, allowing neurons to self-select out of the model through gradient- based equilibrium dynamics. This end-to-end formulation eliminates the need for discrete pruning phases and provides a principled explanation for neuron removal. 9.2 Role of L1 vs L2 Costs The results clearly show that L1 penalties alone reduce participation magnitudes but rarely induce exact collapse to zero. In contrast, L2 penalties are essential for producing sparse equilibria, as they encourage exact zero participation. The combined L1+L2 configuration achieves the best accuracy–sparsity balance, consistent with elastic-net-style regularization effects. 9.3 Numerical Stability Considerations As participation variables approach zero, effective weight matrices may become ill-conditioned. Monitoring condition numbers during training provides a practical safeguard against numerical instability, particularly in deeper architectures. While MNIST experiments remain stable without
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https://arxiv.org/pdf/2512.22106
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2512.22106
| 10
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explicit conditioning, this consideration becomes increasingly important for scaling the method to larger networks. 10 Conclusion In this work, we proposed a game-theoretic perspective on neural network pruning, reframing sparsity as an equilibrium outcome of strategic interaction among parameter groups rather than as an externally imposed constraint. By modeling neurons as players that balance contribution against redundancy, we showed that pruning naturally emerges when continued participation becomes a dominated strategy at equilibrium. Our theoretical analysis established that sparse solutions arise under mild conditions, and our experiments on MNIST validated this prediction. Participation variables evolve smoothly during training, collapsing for redundant neurons and producing highly sparse networks without explicit importance scores or multi-stage pruning pipelines. The observed bimodal participation distributions further support the interpretation of pruning as a stable equilibrium phenomenon. This formulation provides a unifying explanation for several existing pruning heuristics while offering a principled foundation for new algorithmic designs. Beyond neuron-level pruning, the proposed framework naturally extends to structured pruning, dynamic training-time sparsification, and alternative game formulations. Limitations. Our experimental evaluation is intentionally limited to a controlled MNIST setting to isolate equilibrium behavior. While the proposed formulation is general, scaling to deeper architectures and larger datasets may introduce additional optimization and numerical challenges, which we leave for future work. References [1] Y. LeCun, J. S. Denker, S. Solla, R. E. Howard, and L. D. Jackel. Optimal brain damage. In NIPS, 1990. [2] B. Hassibi, D. G. Stork, and G. J. Wolff. Optimal brain surgeon and general network pruning. In IEEE International Conference on Neural Networks, 1993. [3] S. Han, J. Pool, J. Tran, and W. J. Dally. Learning both weights and connections for efficient neural networks. In Conference on Neural Information Processing Systems (NeurIPS), 2015. [4] S. Hanson and L. Pratt. Comparing biases for minimal network construction with back- propagation. In NIPS, vol. 1, 1988. [5] C. Louizos, M. Welling, and D. P. Kingma. Learning sparse neural networks through L0 regularization. arXiv preprint arXiv:1712.01312, 2017. [6] M. C. Mozer and P. Smolensky. Using relevance to reduce network size automatically. Connec- tion Science, 1(1):3–16, 1989. [7] T. Li, et al. Compressing convolutional neural networks via factorized convolutional filters. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019. [8] H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf. Pruning filters for efficient convnets. In ICLR, 2017. [9] Y. He, G. Kang, X. Dong, Y. Fu, and Y. Yang. Soft filter pruning for accelerating deep convolutional neural networks. In IJCAI, pp. 2234–2240, 2018. [10] D. C. Mocanu, E. Mocanu, P. Stone, P. H. Nguyen, M. Gibescu, and A. Liotta. Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), 2018. [11] U. Evci, Y. A. Ioannou, C. Keskin, and Y. Dauphin. Gradient flow in sparse neural networks and how lottery tickets win. In AAAI, 2022.
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 11
|
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2512.22106_page_12
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] |
https://arxiv.org/pdf/2512.22106
|
2512.22106
| 12
|
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[]
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