arXiv:2512.23075v5 Announce Type: replace Abstract: Policy gradient methods for Large Language Models optimize a policy $\pi_\theta$ via a surrogate objective computed from samples of a rollout policy $\pi_{\text{roll}}$. However, modern LLM-RL pipelines suffer from unavoidable implementation divergences -- backend discrepancies, Mixture-of-Experts routing discontinuities, and distributed training staleness -- causing off-policy mismatch ($\pi_{\text{roll}} \neq \pi_\theta$) and approximation errors between the surrogate and the true objective. We demonstrate that classical trust region bounds
The rapid advancement of Large Language Models and their integration into reinforcement learning pipelines is uncovering fundamental limitations in current optimization methods.
Improving LLM-RL optimization is crucial for developing more robust, reliable, and capable AI agents, directly impacting their deployment and utility.
This research outlines a pathway to more stable and efficient LLM reinforcement learning, potentially closing critical performance gaps in AI agent development.
- · AI Research Labs
- · Developers of LLM-based autonomous agents
- · SaaS companies adopting AI agents
- · Companies relying on outdated LLM optimization techniques
Enhanced learning stability and performance for complex LLM-driven tasks.
Accelerated development and broader adoption of sophisticated autonomous AI agents in various industries.
Increased competition among foundational model providers to offer more stable and performant RL-fine-tuned models.
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Read at arXiv cs.LG