新闻
ABSeeker: Training Long-Horizon Search Agents via Answer-Backtracked Credit Assignment
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- ABSeeker introduces answer-backtracked credit assignment to train search agents by converting sparse trajectory outcomes into dense step-level rewards for individual actions.
- 为何重要
- Relevant for engineers building multi-step reasoning systems, web search agents, or question-answering systems that struggle with credit assignment across long action sequences.
- 注意
- Results use only 8.5k training examples on specific benchmarks; generalization to other domains and scalability with larger models remain unclear from this abstract.
- agent
- fine-tun
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