English(EN) Online Learning with LLM Experts from Limited Feedback

新算法支持在有限反馈下进行LLM专家路由

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-05 00:00

研究人员开发了用于大规模语言模型（LLM）专家在线学习的新算法，特别针对有限反馈场景。所提出的方法将路由到不同LLM专家的提示视为一个上下文老虎机问题。实验表明，即使在反馈受限的情况下，这些算法也能有效地学习有效的路由策略，并达到次线性遗憾界限。 AI

影响这项研究可能带来更高效、更自适应的LLM系统，这些系统在训练和微调时需要的数据更少。

排序理由该条目是一篇研究论文，详细介绍了LLM在线学习的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-09-05 00:00

LLM专家在有限反馈下进行在线学习

Adaptive routing of prompts to LLM experts is formulated as a contextual bandit problem with limited feedback, yielding algorithms with sublinear regret bounds and effective routing strategies.