arXiv:2605.28150v1 Announce Type: new Abstract: Large scale reinforcement learning has become a central tool for improving reasoning in large language models. At this scale, generation is often lagged or asynchronous, so updates are performed on data collected by older policies. This makes learning inherently off-policy. Most existing approaches nevertheless remain rooted in PPO-style trust-region objectives, treating training as approximately on-policy and using importance weights to correct distribution mismatch. These corrections can introduce high variance, destabilize optimization, and ac
The paper addresses a critical technical challenge in large-scale reinforcement learning for LLMs, which is a rapidly evolving field.
Improving off-policy learning for LLMs enhances their reasoning capabilities, accelerating development and deployment of more advanced AI.
This research provides a more stable and effective method for training large language models with reinforcement learning, potentially leading to faster and more reliable model improvements.
- · AI researchers
- · LLM developers
- · Companies using LLMs
- · AI infrastructure providers
- · AI approaches relying solely on on-policy methods without robust off-policy corr
More robust and efficient training of large language models for reasoning tasks.
Accelerated development of AI agents capable of complex reasoning and task execution.
Enhanced automation and transformation of white-collar workflows through more capable AI systems.
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Read at arXiv cs.LG