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新方法提升智能体强化学习效率 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了两种新方法来提高语言智能体强化学习（RL）的效率。EnvSched通过从回放历史中学习来优化任务实例和环境的调度，优先考虑信息丰富的奖励差异并确保环境多样性。ScaleRLT通过实现精确的对数概率和更快的延迟回放，解决了RL中循环Transformer的挑战，提高了GSM8K等基准测试的吞吐量和准确性。 AI

影响 这些在RL效率方面的进步可以加速开发和部署更强大的语言智能体。

排序理由 arXiv上发表了两篇研究论文，详细介绍了提高强化学习效率的新方法。

在 arXiv cs.CL 阅读 →

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新方法提升智能体强化学习效率 · 跟踪2个来源

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Sihan Yang, Weichen Bai, Ruichuan An, Yiming Cheng, Zhiyuan Feng, Tianyi Jiang, Renye Yan, Ning Ding, Yifan Zhang, Renrui Zhang, Yehui Tang, Wentao Zhang ·

    EnvSched：面向推出高效智能体强化学习的环境感知调度

    arXiv:2610.37609v1 Announce Type: cross Abstract: As executable environments for language agents scale, generating and verifying their rollouts becomes increasingly expensive. Under finite rollout budgets, this raises an important question: can data management at the environment …

  2. arXiv cs.LG TIER_1 English(EN) · Dezhen Lu, Zixiang Ni, Runguo Li ·

    ScaleRLT：用于循环Transformer强化学习的精确对数概率和快速延迟Rollouts

    arXiv:2610.34427v1 Announce Type: new Abstract: Looped language models such as Ouro reuse one block of layers several times per token. A rollout engine can batch the deeper loops of several response positions and run them only when a deep readout is due. This deferred schedule is…

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