English(EN) Learning to Solve Hard Problems in RL for LLMs by Never Giving Up

新的强化学习方法解决了LLM训练中的“马太效应”

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一篇新的研究论文介绍了“LLM强化学习中的马太效应”，该效应观察到强化学习（RL）不成比例地提高了大型语言模型（LLM）在简单问题上的表现，而对更难的问题几乎没有改进。为了解决这个问题，该论文提出了“永不放弃”（NGU），一种自适应采样方法，它会持续为问题生成样本，直到找到正确的解决方案。这种方法动态地重新分配计算资源，优先处理更难的问题，并在Deepscaler和编码任务等基准测试中展示了每计算单位的改进性能。 AI

影响这项研究可能导致更有效和更高效的LLM训练，特别是对于需要大量计算资源的复杂任务。

排序理由该集群包含一篇详细介绍LLM训练新方法的学术论文。[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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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍LLM训练新方法的学术论文。[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) · Michael Noukhovitch, Hamish Ivison, Nathan Lambert, Aaron Courville · 2026-09-15 04:00

通过永不放弃来学习为LLM解决RL中的难题

arXiv:2609.13443v1 Announce Type: cross Abstract: We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call…

本文如何被排名

报道来源 [1]

通过永不放弃来学习为LLM解决RL中的难题

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