Reproduce “RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning” on OpenResearch
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About this paper
Yongqiang Yao, Jinru Tan, Kaihuan Liang, Zixin Yin, Yazhe Niu, Ruihao Gong, Dahua Lin, Ningyi Xu
RollVerify improves long-context reinforcement learning by checking unfinished responses after policy updates and retaining usable prefixes while trimming stale suffixes. It addresses rollout stragglers, which occupy 70–80% of a typical on-policy pipeline. On DAPO-MATH, Qwen3-8B-Base reaches 57.2 average accuracy versus 57.3 for on-policy GRPO, with training cost reduced from 98.1 to 57.5 GPU-days; evidence is from colocated math experiments.
Key insights
- Assessing mismatch directly from policy probabilities is more informative than judging a rollout by its age.
- Sequence-level and token-level checks complement each other: token-level verification can catch shifted tokens hidden by segment averages.
- The efficiency advantage grows with response context length, where long-tail rollout delays are more costly.
Summary by alphaXiv.
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