Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
TL;DR - Ecdysis trains self-evolving runtime harnesses for LLM agents by aggregating failures across tasks and prioritizing systematic harness defects over model-specific errors. It reportedly improves harness training speed by up to 1.84Ă— while increasing reasoning accuracy by 18.56%.
- Replaces repeated per-instance search with batch-level, cross-instance failure analysis.
- Separates model-specific deficiencies from recurring problems that warrant harness-level repair.
- Uses Failure-Driven Collaborative Refinement with multiple diagnostic roles to refine modification specifications iteratively.
- Aims to reduce training overhead and avoid overfitting harnesses to individual tasks or failure patterns.