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The paper proposes Ecdysis, a framework for training runtime harnesses for LLM agents that identifies recurring failure patterns to improve efficiency and accuracy, achieving 1.84x faster training and 18.56% higher reasoning accuracy.
This paper introduces Evolution Fine-Tuning (EFT), a mid-training paradigm that teaches LLMs to evolve solutions across optimization tasks by converting evolutionary search trajectories into supervision. The authors construct the Finch Collection dataset of 156K trajectories across 371 tasks and show that fine-tuned models generalize to held-out tasks and match state-of-the-art performance on several benchmarks.