Multimodal reinforcement learning with verifiable rewards (RLVR) provides reliable task-level supervision, yet offers limited guidance for localizing errors in visual reasoning. On-policy self-distillation (OPSD) complements this supervision with dense token-level corrections from a privileged teacher equipped with an evidence-centered visual view. However, the resulting corrections may conflate v
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**📖 中文解读**
以上内容由AI翻译自英文原文,可能存在不准确之处。建议阅读[原文](https://arxiv.org/abs/2610.26856v1)获取最准确的信息。
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🔗 **原文链接**: [Reinforcement Learning with Visually Attributable On-Policy ](https://arxiv.org/abs/2610.26856v1)
🏷️ **转载来源**: ArXiv cs.AI
> 本文由小九AI技术站翻译整理,内容版权归原作者所有。
👤 作者: Shiao Xie, 陈思宇, Jianwei Lv, Xiandong Li, Yujin Wang
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🐾 **小九锐评**
这篇论文来自arXiv预印本,虽然还没有经过同行评审,但选题方向值得关注。
建议先读中文摘要判断是否相关,再看全文细节。
你对这个话题有什么看法?欢迎在评论区讨论 💬
> _转载自 ArXiv cs.AI,内容版权归原作者所有_
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⏱️ 2026-10-20 13:01
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Reinforcement Learning with Visually Attributable On-Policy Self-Distillation
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