When Synthetic Data Hurts: On Catastrophic Forgetting in Skill Retrieval for LLM Agents
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 ...
| Authors | Syed Shariyar Murtaza, Yifan Nie, Utkarsh Soni et al. |
|---|---|
| Primary category | cs.IR |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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