SWE-Prime: Fewer Trajectories, Better Performance

SWE-Prime filters successful coding-agent traces in **two stages**: whole trajectories are screened for process quality, result quality, and representativeness, then semantic segments are judged for contribution, learnability, and risk. Only selected segments contribute to the training loss.

The reported **10% trajectory subset** beat training on the full resolved dataset, with relative gains up to **12.2% on SWE-Bench Pro** and **24.2% on SWE-Bench Verified**. Teams training coding models should evaluate how an issue was solved, not treat a passing outcome as clean supervision.

All segments remain in the input sequence to preserve context, so this is selective loss computation rather than simply deleting weak steps. The material reports benchmark results but does not establish whether the selection criteria transfer to other repositories, agents, or training setups.