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Self-Play Meets Skill Evolution: Self-Evolving Search Agents that Pose, Solve, and Remember
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- SESA combines self-play with evolving skill memory for question-answering agents. A challenger poses problems while a solver retrieves learned skills, with failures distilled back into memory.
- 为何重要
- Relevant for engineers building search-based QA systems or training agents through self-play who want to improve accuracy on multi-hop reasoning tasks.
- 注意
- Paper shows improvements of 1.2 to 3.2 points on benchmarks, but real-world gains depend on task domain and whether external memory retrieval is feasible at inference time.
收听本摘要
- agent
- distill
- benchmark
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