SWE-Prime: Fewer Trajectories, Better Performance
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Observed public metrics from 1 member.
Merged summary
TL;DR - SWE-Prime is a two-stage data-selection method for supervised fine-tuning of software-engineering agents that filters successful trajectories by quality and representativeness, then selects useful segments for loss computation. Using only 10% of trajectories outperformed training on the full resolved dataset, showing that cleaner supervision can beat greater data volume.
- Screens trajectories using process quality, result quality, and dataset representativeness.
- Evaluates semantic step segments for solution contribution, learnability, and potential risks.
- Retains all segments as context during training but computes loss only on selected segments.
- Achieved relative gains of up to 12.2% on SWE-Bench Pro and 24.2% on SWE-Bench Verified.
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SWE-Prime: Fewer Trajectories, Better Performance
TL;DR - SWE-Prime is a two-stage data-selection method for supervised fine-tuning of software-engineering agents that filters successful trajectories by quality and representativeness, then selects useful segments for loss computation. Using only 10% of trajectories outperformed training on the full resolved dataset, showing that cleaner supervision can beat greater data volume.
- Screens trajectories using process quality, result quality, and dataset representativeness.
- Evaluates semantic step segments for solution contribution, learnability, and potential risks.
- Retains all segments as context during training but computes loss only on selected segments.
- Achieved relative gains of up to 12.2% on SWE-Bench Pro and 24.2% on SWE-Bench Verified.