arXiv:2605.21654v1 Announce Type: new Abstract: Reinforcement learning substantially improves pretrained language models, but it remains understudied why critic-free methods such as PPO and GRPO work as well as they do, and when they should provide the largest gains. We develop a value-gradient perspective of critic-free RL for LLM post-training. First, under a differentiable rollout and additive-noise parameterization, we show that the actor update is value-gradient-like in expectation: the backward pass propagates costates whose conditional expectation equals the value gradient. Second, for
This research emerges as methods like PPO are widely adopted but their theoretical underpinnings for LLMs remain less understood, creating a need for deeper explanatory models.
Understanding the core mechanisms of RL for LLMs can lead to more efficient training, better performance, and unlock new capabilities in general AI models.
This research provides a theoretical framework that explains the efficacy of critic-free RL methods, potentially guiding future optimization and development of LLM post-training techniques.
- · AI researchers
- · Large Language Model developers
- · AI-driven product companies
- · AI models relying on suboptimal RL methods
Improved understanding of RL's effectiveness in LLM training allows for more targeted development of optimization algorithms.
More robust and efficient LLMs could emerge, accelerating the deployment and capability of AI agents across various industries.
Deeper theoretical insights might lead to new architectures or training paradigms that significantly reduce compute requirements for advanced models.
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Read at arXiv cs.LG