16 new research on inference-time scaling:
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16 new research on inference-time scaling:
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For the last couple of weeks a large amount of studies on inference-time scaling has emerged. And it's so cool, because each new paper adds a trick to the toolbox, making LLMs more capable without needing to scale parameter count of the models.
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For the last couple of weeks a large amount of studies on inference-time scaling has emerged. And it's so cool, because each new paper adds a trick to the toolbox, making LLMs more capable without needing to scale parameter count of the models.
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So here are 13 new methods + 3 comprehensive studies on test-time scaling:
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So here are 13 new methods + 3 comprehensive studies on test-time scaling:
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1. 
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1. 
Inference-Time Scaling for Generalist Reward Modeling (2504.02495)
https://huggingface.co/papers/2504.02495
2504.02495
paper
resource
https://huggingface.co/papers/2504.02495
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Probably, the most popular study. It proposes to boost inference-time scalability by improving reward modeling. To enhance performance, DeepSeek-GRM uses adaptive critiques, parallel sampling, pointwise generative RM, and Self-Principled Critique Tuning (SPCT)
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Probably, the most popular study. It proposes to boost inference-time scalability by improving reward modeling. To enhance performance, DeepSeek-GRM uses adaptive critiques, parallel sampling, pointwise generative RM, and Self-Principled Critique Tuning (SPCT)
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2. 
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T1: Tool-integrated Self-verification for Test-time Compute Scaling in
  Small Language Models (2504.04718)
https://huggingface.co/papers/2504.04718
2504.04718
paper
resource
https://huggingface.co/papers/2504.04718
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Allows small models to use external tools, like code interpreters and calculator, to enhance self-verification
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Allows small models to use external tools, like code interpreters and calculator, to enhance self-verification
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3. 
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Z1: Efficient Test-time Scaling with Code (2504.00810)
https://huggingface.co/papers/2504.00810
2504.00810
paper
resource
https://huggingface.co/papers/2504.00810
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Proposes to train LLMs on code-based reasoning paths to make test-time scaling more efficient, limiting unnecessary tokens with a special dataset and a Shifted Thinking Window
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Proposes to train LLMs on code-based reasoning paths to make test-time scaling more efficient, limiting unnecessary tokens with a special dataset and a Shifted Thinking Window
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4. 
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4. 
GenPRM: Scaling Test-Time Compute of Process Reward Models via
  Generative Reasoning (2504.00891)
https://huggingface.co/papers/2504.00891
2504.00891
paper
resource
https://huggingface.co/papers/2504.00891
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Introduces GenPRM, a generative PRM, that uses CoT reasoning and code verification for step-by-step judgment. With only 23K training examples, GenPRM outperforms prior PRMs and larger models
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Introduces GenPRM, a generative PRM, that uses CoT reasoning and code verification for step-by-step judgment. With only 23K training examples, GenPRM outperforms prior PRMs and larger models
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5. 
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5. 
Can Test-Time Scaling Improve World Foundation Model? (2503.24320)
https://huggingface.co/papers/2503.24320
2503.24320
paper
resource
https://huggingface.co/papers/2503.24320
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SWIFT test-time scaling framework improves World Models' performance without retraining, using strategies like fast tokenization, Top-K pruning, and efficient beam search
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SWIFT test-time scaling framework improves World Models' performance without retraining, using strategies like fast tokenization, Top-K pruning, and efficient beam search
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6. 
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6. 
Relevance Isn't All You Need: Scaling RAG Systems With Inference-Time
  Compute Via Multi-Criteria Reranking (2504.07104)
https://huggingface.co/papers/2504.07104
2504.07104
paper
resource
https://huggingface.co/papers/2504.07104
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Proposes REBEL for RAG systems scaling, which uses multi-criteria optimization with CoT prompting for better performance-speed tradeoffs as inference compute increases
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Proposes REBEL for RAG systems scaling, which uses multi-criteria optimization with CoT prompting for better performance-speed tradeoffs as inference compute increases
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7. 
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7. 
$φ$-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time
  Exploration and Exploitation (2503.13288)
https://huggingface.co/papers/2503.13288
2503.13288
paper
resource
https://huggingface.co/papers/2503.13288
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Proposes a φ-Decoding strategy that uses foresight sampling, clustering and adaptive pruning to estimate and select optimal reasoning steps
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Proposes a φ-Decoding strategy that uses foresight sampling, clustering and adaptive pruning to estimate and select optimal reasoning steps
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Read further below 👇
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Read further below 👇
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Also, subscribe to the Turing Post 
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Also, subscribe to the Turing Post 
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16 new research on inference-time scaling:

For the last couple of weeks a large amount of studies on inference-time scaling has emerged. And it's so cool, because each new paper adds a trick to the toolbox, making LLMs more capable without needing to scale parameter count of the models.

So here are 13 new methods + 3 comprehensive studies on test-time scaling:

1. https://huggingface.co/papers/2504.02495
Probably, the most popular study. It proposes to boost inference-time scalability by improving reward modeling. To enhance performance, DeepSeek-GRM uses adaptive critiques, parallel sampling, pointwise generative RM, and Self-Principled Critique Tuning (SPCT)
 
2. https://huggingface.co/papers/2504.04718
Allows small models to use external tools, like code interpreters and calculator, to enhance self-verification

3. https://huggingface.co/papers/2504.00810
Proposes to train LLMs on code-based reasoning paths to make test-time scaling more efficient, limiting unnecessary tokens with a special dataset and a Shifted Thinking Window

4. https://huggingface.co/papers/2504.00891
Introduces GenPRM, a generative PRM, that uses CoT reasoning and code verification for step-by-step judgment. With only 23K training examples, GenPRM outperforms prior PRMs and larger models

5. https://huggingface.co/papers/2503.24320
SWIFT test-time scaling framework improves World Models' performance without retraining, using strategies like fast tokenization, Top-K pruning, and efficient beam search

6. https://huggingface.co/papers/2504.07104
Proposes REBEL for RAG systems scaling, which uses multi-criteria optimization with CoT prompting for better performance-speed tradeoffs as inference compute increases

7. https://huggingface.co/papers/2503.13288
Proposes a φ-Decoding strategy that uses foresight sampling, clustering and adaptive pruning to estimate and select optimal reasoning steps

Read further below 👇

Also, subscribe to the Turing Post https://www.turingpost.com/subscribe
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