Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
[Sztuczna inteligencja] Autorzy: Yiyang Feng, Biddut Sarker Bijoy, Niranjan Balasubramanian i 1 więcej
Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In practice, induced skills may transfer unreliably and can even harm the agent that retrieves them. When agent-induced skills transfer reliably across tasks remains an open question. We conduct a comprehensive and controlled study of how the way skills are induced shapes their transfer across tasks. Specifically, we compare task-level with subtask-level skill induction and text with code skill formats, the two axes along which existing methods differ. Task-level skills mostly reduce the agent's performance below its no-memory baseline while subtask-level skills raise it above on average, and text skills transfer better than code skills. To further understand our findings, we examine two complementary properties of the induced skills: specificity, which mea…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja