English(EN) Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents

新框架通过中间奖励改进LLM搜索代理训练

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了一个新的训练框架，以提高大型语言模型（LLM）使用的搜索代理的效率。该框架通过在最终结果奖励之外，纳入来自检索步骤的中间监督信号，来解决强化学习中的信用分配挑战。实验表明，这种方法提高了代理的整体性能，并突显了奖励设计和信用分配在训练有效的搜索代理中的重要性。 AI

影响增强LLM在复杂信息检索和多跳问答中的能力。

排序理由详细介绍LLM搜索代理新训练框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍LLM搜索代理新训练框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

arXiv cs.CL TIER_1 English(EN) · Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis, Ruofei Lai, Jeff Z. Pan · 2026-10-08 04:00

超越结果奖励：为搜索代理构建和分配检索信用

arXiv:2610.10179v1 Announce Type: new Abstract: Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising approach for post-training such …