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English(EN) Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR

新的 DATPO 方法增强了大型推理模型的推理覆盖范围

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的 DATPO 方法，以提高使用可验证奖励强化学习 (RLVR) 训练的大型推理模型的推理能力。DATPO 通过优化训练 rollout 来解决 RLVR 在扩展内在推理覆盖范围 (pass@k) 方面的局限性。该方法结合了自适应难度树搜索和句子熵引导的分叉，以最大化语义多样性并克服局部化问题，在数学推理基准测试中优于现有方法。 AI

影响 增强了大型语言模型的推理覆盖范围，有可能提高在复杂任务上的性能。

排序理由 该集群包含一篇学术论文，详细介绍了一种改进 AI 模型推理的新方法。

在 arXiv cs.AI 阅读 →

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新的 DATPO 方法增强了大型推理模型的推理覆盖范围

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Youngjun Yu, Sanghwan Jang, Hwanjo Yu ·

    面向RLVR中扩展推理覆盖的难度自适应树状策略优化

    arXiv:2609.08650v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been central to the recent success of Large Reasoning Models. However, while RLVR significantly improves single-sample accuracy, it often fails to expand the model's intrin…

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

    面向RLVR中扩展推理覆盖的难度自适应树状策略优化

    DATPO improves reasoning coverage in large models by using difficulty-adaptive tree-structured rollouts with sentence-entropy-guided branching and diversity-aware optimization.

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