ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment· ABSeeker: 通过答案反向追踪进行长时搜索代理训练
Long-horizon search agents must make multiple sequential actions (steps) to search, retrieve, verify, and integrate evidence to reach a final answer. However, existing methods for training these agents typically treat all steps within a trajectory uniformly during both supervised fine-tuning (SFT) and reinforcement learning (RL), failing to distinguish useful actions from erroneous or redundant ones. In this paper, we propose Answer-Backtracked Credit Assignment (ABC), a fine-grained credit assignment framework for training long-horizon search agents by converting sparse trajectory-level outcomes into dense step-level supervision that rewards useful actions (even in failed trajectories) while suppressing erroneous or redundant actions. Specifically, given a potentially obscure query and it
长时搜索代理通过答案反向追踪训练方法，提升行动效率。
- 核心方法
- 提出了一种答案反向追踪信用分配(ABC)框架，将稀疏的轨迹级别结果转化为密集的动作级别监督。
- 适合谁读
- 适合研究者和工程师阅读
- 要解决的问题
- 现有长时搜索代理训练方法无法区分轨迹中的有用与无用动作，导致训练效率低下。
- 关键实验
- 对长时搜索任务进行了实验，展示了ABC框架相对于传统方法的性能提升。
- 主要贡献
- 通过精细化信用分配，即使在失败的轨迹中也能奖励有用的动作，并抑制错误或冗余的动作。
- 意义与局限
- 可以提高长时搜索代理的训练效率和质量，但可能对计算资源要求较高。