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Dense rewards in language model training may not be worth the compute cost

By PulseAugur Editorial · [2 sources] ·

A new study on language model reasoning suggests that dense process rewards, such as Monte-Carlo rewards, may not be as effective as initially thought when evaluated under a matched-compute protocol. The research found that these dense rewards underperformed standard GRPO when considering the computational cost in terms of generated tokens and training time. Instead, training language models on prompts with a predicted success rate near 1/2 showed small but consistent accuracy improvements. AI

IMPACT This research suggests a re-evaluation of reward mechanisms in LLM training, potentially leading to more efficient and effective reasoning capabilities.

RANK_REASON The cluster contains an academic paper detailing novel research findings and methodologies in AI.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Dense rewards in language model training may not be worth the compute cost

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Vikash Singh, Weicong Chen, Debargha Ganguly, Yanyan Zhang, Mohsen Hariri, Sreehari Sankar, Wang Yang, Chaoda Song, Alexander Nemecek, Osama Zafar, Yuting Shao, Kai Ye, Biyao Zhang, Xinpeng Li, Shouren Wang, Erman Ayday, Jing Ma, Sanmukh Rao Kuppannagari… ·

    Are Dense Rewards Worth Their Tokens in GRPO?

    arXiv:2611.13560v1 Announce Type: cross Abstract: RLVR trains language models to reason by rewarding correct final answers. A widely used algorithm, group-relative policy optimization (GRPO), gives every token of a solution the same credit, so dense process rewards have been prop…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Are Dense Rewards Worth Their Tokens in GRPO?

    At matched compute, dense process rewards such as Monte-Carlo rewards underperform standard GRPO for language-model reasoning, while selecting prompts with predicted success near 1/2 yields small consistent accuracy gains.

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