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,
The rapid development and deployment of LLMs necessitate more robust and efficient training methods, making advancements in RL stability and performance critical for their continued evolution.
Improving state value estimation in LLM reinforcement learning can lead to more stable, effective, and less resource-intensive training, accelerating the development of advanced AI models.
Current RL approaches for LLM training that suffer from 'critic collapse' may be overcome, leading to more sophisticated and generalizable AI capabilities.
- · AI developers
- · LLM companies
- · AI research institutions
- · Companies relying on less efficient RL training methods
More stable and performant LLM training leads to more capable and reliable AI models.
Accelerated progress in LLM capabilities could broaden their applications and societal integration.
Improved fundamental AI training techniques may reduce compute requirements per quality unit, impacting the energy footprint of advanced AI.
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