Hista and Numca: Estimate State Value Effectively for LLM Reinforcement Learning

arXiv:2605.29782v1 Announce Type: new Abstract: Reinforcement learning (RL) refines large language models (LLMs) by directly optimizing model behavior through reward signals. While accurate state value estimation is critical for stable training in classical RL, it remains an underexplored challenge in LLM post-training. In this work, we introduce the State Value Estimation Benchmark (SVEB) to assess state estimation within existing RL frameworks and show that critics in standard approaches like PPO collapse to a coarse group-average baseline. To address this, we propose two techniques: Numca,

Source: arXiv cs.LG — read the full report at the original publisher.

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