新的Owen-Shapley RL算法改进了LLM在搜索中的信用分配

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员推出了一种新颖的强化学习框架Owen-Shapley Policy Optimization (OSPO)，旨在解决用于个性化推荐任务的大型语言模型中的信用分配问题。标准方法难以识别哪些特定token有助于高质量输出，尤其是在从不明确的语言推断用户意图时。OSPO根据token的边际贡献重新分配序列级奖励，在不要求参数化价值模型的情况下进行片段级信用分配。 AI

影响这种新的RL算法可以通过更好地将信用归因于特定的输出片段来提高LLM在推荐任务中的性能。

排序理由这是一篇详细介绍LLM新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Abhijnan Nath, Alireza Bagheri Garakani, Tianchen Zhou, Fan Yang, Yan Gao, Nikhil Krishnaswamy · 2026-05-08 04:00

Owen-Shapley 策略优化：面向生成式搜索 LLM 的原则性强化学习算法

arXiv:2601.08403v2 Announce Type: replace Abstract: Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewards. These obscure which tokens actually contribu…

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报道来源 [1]

Owen-Shapley 策略优化：面向生成式搜索 LLM 的原则性强化学习算法

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