When Synthetic Data Hurts: On Catastrophic Forgetting in Skill Retrieval for LLM Agents

Abstract:LLM agents increasingly rely on external skills retrieved at runtime, making skill selection from large repositories a critical challenge. We present a production skill router over 34,396 skills and a large-scale study of skill retrieval using limited real supervision and synthetic data. We found that the synthetic-data fine-tuning improves in-distribution retrieval but it causes catastrophic forgetting on real and out-of-distribution (OOD) data. We evaluate several forgetting mitigation fine-tuning approaches inspired by continual learning, including embedding-anchor regularization, Learning without Forgetting (LwF), Elastic Weight Consolidation (EWC), and L2-initialization. The results show that these approaches not only retain the performance on OOD skills retrieval but also improve the retrieval on synthetic in-distribution skills by 13.98\% for 0.6B Qwen retriever and reranker. Our results provide a practical benchmark and a robust fine-tuning recipe for scarce, multi-positive supervision.

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.10750 [cs.IR]

(or arXiv:2609.10750v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2609.10750 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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