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English(EN) Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization

新的近端KL方法提高了Qwen3模型上策略离轨RLVR的准确性

研究人员开发了近端KL(Proximal KL),一种用于策略离轨可验证奖励强化学习(RLVR)的新颖方法。该技术通过使用Token级重要性加权和有界未来KL信用,解决了异步生成和响应重用等挑战,避免了对全响应权重或拟合评论家的需求。在八个数学基准测试中,与Qwen3-1.7B和Qwen3-4B模型上的GRPO相比,近端KL在微平均准确性方面表现出更高的准确性,优于GRPO高达2.58%。 AI

影响 这种新方法可以提高训练AI模型的效率和准确性,特别是在涉及策略离轨数据的情况下。

排序理由 该集群包含一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的近端KL方法提高了Qwen3模型上策略离轨RLVR的准确性

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该集群包含一篇详细介绍强化学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    在具有近端KL正则化的离策略RLVR中实现Token级重要性采样

    Off-policy data in reinforcement learning with verifiable rewards (RLVR) can arise from asynchronous generation, rollout-training mismatch, and response reuse. Token-wise importance weighting avoids multiplying likelihood ratios across a response but generally biases policy gradi…