ACEBench-Agent

CurateEvo: Failure-Driven Dynamic Data Curation for Agentic LLM Post-Training

CurateEvo is a new framework for agentic post-training that treats data curation as a dynamic, evolving process rather than a fixed preprocessing step. The system represents curation strategies as executable code and iteratively rewrites them based on failed trajectories from a held-out development set, producing SFT data, RL data, and an inference-time memory bank. Evaluated on ACEBench-Agent, BFCL-V4, and τ²-Bench, CurateEvo outperforms prior curation methods by 3.2 and 2.7 average points in labeled and wild-data settings respectively, while also reducing curation overhead.