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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 ฯ-Decoding strategy that uses foresight sampling, clustering and adaptive pruning to estimate and select optimal reasoning steps
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