English(EN) Environmental Feedback Modeling Matters: Rethinking Feedback Treatment in Agentic Hindsight Self-Distillation

新的SELF框架通过环境反馈增强语言代理训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-09 04:00

研究人员引入了一个名为SELF（SELF-distilLation with environmental Feedback modeling）的新框架，用于改进在缺乏直接奖励的交互式环境中训练的语言代理。该框架联合优化环境反馈建模和事后自我蒸馏，使代理能够在从条件化反馈的自我教师那里学习的同时，预测环境响应。实验表明，SELF在tau-Bench和AppWorld等基准测试中优于SDPO和GRPO等现有方法，通过更有效地利用环境反馈来增强代理能力。 AI

影响增强了在缺乏直接奖励的环境中代理的能力，有可能提高复杂交互任务的性能。

排序理由该集群包含一篇详细介绍新框架和语言代理训练实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Hangxi Guo, Fengyuan Liu, Yue Wang, Yuhua Qi, Haoyi Xiong, Fei Sun, Mengnan Du · 2026-10-09 04:00

环境反馈建模很重要：重新思考代理式事后自蒸馏中的反馈处理

arXiv:2610.11384v1 Announce Type: new Abstract: Reinforcement learning is commonly used to train language agents in interactive environments, but cannot be directly applied when rewards are unavailable. Recent methods use environmental feedback as privileged context for hindsight…

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报道来源 [1]

环境反馈建模很重要：重新思考代理式事后自蒸馏中的反馈处理

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