A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization

Prasanth YSS, Zhichen Ren, Rasa Hosseinzadeh, Ilan Gofman, Yuqi Chen, Zhaoyan Liu, Guangwei Yu, Jesse Cresswell, Satya Krishna Gorti

A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization: 2 upvotes on Hugging Face Daily Papers, #21 of 31 papers on 2026-06-17. Day-by-day upvote history. It lost 7 votes when the Hub removed votes in bulk.

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 objective that updates only on positive-advantage completions. Across mathematical reasoning and multi-hop QA benchmarks, WAPO improves training stability and matches or outperforms baselines across multiple model families. Full code can be found at https://github.com/layer6ai-labs/wapo.

Paper page on Hugging Face · arXiv

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