arXiv:2606.16154v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but GRPO-style optimization remains prone to collapse. We analyse this instability through token-level gradient dynamics, deriving a taxonomy that predicts how updates affect next-token probabilities and entropy. The taxonomy shows that stability depends jointly on the advantage sign and token distribution under the current policy. Motivated by this finding, we propose Winner Advantage Policy Optimization (WAPO), a simple online clipped policy-gradient object
This paper addresses a known instability in a specific type of reinforcement learning for large language models, suggesting a timely improvement in their training methods.
Improved stability and optimization in RLVR can lead to more robust and reliable AI systems, particularly for reasoning tasks, impacting the overall performance and application of advanced language models.
New policy optimization techniques like WAPO could make the development of verifiable and reasoning-capable AI more efficient and predictable, reducing training failures.
- · AI researchers
- · Large language model developers
- · Companies deploying advanced AI
- · Developers relying on unstable optimization methods
More stable and efficient training of large language models for complex reasoning tasks.
Accelerated development and broader adoption of AI agents capable of higher-fidelity reasoning and task execution.
Enhanced trust and reliability in AI systems, potentially influencing regulatory frameworks and public perception of AI capabilities.
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Read at arXiv cs.LG