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新的RACE框架通过自适应奖励演进增强强化学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为RACE（Reward-Adaptive Co-Evolution）的新框架，以改进开放式任务中的强化学习。该框架通过动态适应奖励的信息量，解决了静态评分标准和固定协同演进计划的局限性。RACE对训练数据组进行分类，仅用信息性信号更新策略，并根据奖励饱和度和改进平台期在策略和评分标准优化阶段之间切换。该系统还包括用于动态调整的在线阈值自适应。实验表明，RACE在多个任务中优于固定交替基线，并增强了评分标准的区分度。 AI

影响 该框架可以提高AI代理在复杂、开放式任务中的训练效率和性能。

排序理由 该集群包含一篇详细介绍强化学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的RACE框架通过自适应奖励演进增强强化学习

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该集群包含一篇详细介绍强化学习新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yizhu Chen, Jianguang Jiang, Jiameng Huang, Chuyu Fang, Bojun Zhang, Zhenyu Jiao, Xing Yuan, Tong Lin ·

    Reward-Adaptive Co-Evolution of Policy and Rubric

    arXiv:2610.40096v1 Announce Type: new Abstract: Rubric-based rewards provide fine-grained, interpretable supervision for open-ended reinforcement learning, where holistic scalar rewards struggle to capture multidimensional response quality. Static rubrics can lose discriminative …

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