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English(EN) Do LLMs Learn from Rewards in Context? : Rethinking the role of reward in In-Context Reinforcement Learning

新研究探讨大型语言模型推理中的过程奖励，并质疑上下文强化学习的有效性

作者 PulseAugur 编辑部 · [3 个来源] ·

一个名为 PROGRS 的新框架已被开发出来，用于改进大型语言模型在数学推理中的强化学习过程奖励的使用。该方法在 arXiv 论文中有所详述，它将过程奖励视为结果组内的相对偏好，而不是绝对目标，以防止奖励攻击并确保结果的正确性仍然占主导地位。在多个基准测试上的实验表明，PROGRS 持续优于仅基于结果的基线。另外，Hugging Face 的一项研究质疑上下文强化学习（ICRL）是否真正起到了强化学习的作用，发现虽然奖励被读取，但其影响很小，这表明 ICRL 更类似于上下文学习（ICL）。 AI

影响 这项研究改进了大型语言模型从反馈中学习的方式，有望带来更强大、更可靠的 AI 代理，同时也质疑了当前上下文学习范式的有效性。

排序理由 该集群包含两篇研究论文，讨论了大型语言模型推理和强化学习方面的进展和基本问题。

在 Hugging Face Daily Papers 阅读 →

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新研究探讨大型语言模型推理中的过程奖励，并质疑上下文强化学习的有效性

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该集群包含两篇研究论文，讨论了大型语言模型推理和强化学习方面的进展和基本问题。
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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Mohammad Rezaei, Jens Lehmann, Sahar Vahdati ·

    LLM 强化学习中面向结果引导推理步骤的奖励处理

    arXiv:2604.02341v2 Announce Type: replace-cross Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers can be checked automatically and converted into reliable training signals…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    LLM强化学习中的思维泄露

    Reinforcement learning gains in hybrid reasoning models' NO-THINK mode largely stem from reactivating existing THINK-mode reasoning rather than learning new capabilities.

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    大型语言模型是否从上下文奖励中学习？：重新思考上下文强化学习中奖励的作用

    LLM agents increasingly improve at inference time by accumulating experience in context rather than by updating parameters. This process is often described as in-context reinforcement learning (ICRL). Whether in-context learning (ICL) can actually play the role of RL, however, ha…

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