arXiv:2606.04560v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards with GRPO is a standard approach for post-training reasoning LLMs. It remains sample inefficient. Each rollout is used for a single gradient update and then discarded. Naive replay is not well suited in this setting because LLM policies drift quickly per gradient step. Stored rollouts therefore become stale and can destabilize training. We propose a rollout-level replay buffer for GRPO that stores and samples individual rollouts rather than whole groups. The buffer bounds staleness through age evicti
The rapid advancement of large language models (LLMs) and the increasing focus on post-training reasoning necessitate more efficient and stable reinforcement learning methods.
Improved sample efficiency and training stability in reinforcement learning for LLMs can accelerate their development and enhance their capabilities for complex tasks.
The proposed rollout-level replay buffer could make reinforcement learning for LLMs more practical and scalable, addressing a significant bottleneck in their training.
- · AI researchers
- · LLM developers
- · Companies deploying LLMs
- · Existing inefficient RL methods
- · Organizations with limited compute resources
Stabilized and faster training of reinforcement learning for LLMs will become more accessible.
More sophisticated and robust LLMs capable of advanced reasoning tasks could emerge sooner.
This could lead to a broader adoption of agentic LLMs in various industries, potentially impacting numerous white-collar workflows.
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Read at arXiv cs.LG