Fine-tuning on 10% of successful agent trajectories beats fine-tuning on all of them.

SWE-Prime's premise is that a successful trajectory still contains ineffective, redundant and risky steps, so SFT on all resolved runs teaches bad habits (arXiv 2608.27449). It filters at trajectory level on process quality, result quality and representativeness, then at segment level by grouping consecutive steps and scoring each on contribution, learnability and risk. All segments stay in the sequence for context, but only selected ones contribute to the loss. The 10% subset beats the full resolved dataset by up to 12.2% on SWE-Bench Pro and 24.2% on Verified.