SWE-Prime: Fewer Trajectories, Better Performance
To improve large language models' ability to resolve real-world software issues, prior work has focused on constructing large-scale agent trajectory datasets and performing supervised fine-tuning (SFT) on successful trajectories. However, task success does not guarantee high-quality supervision: successful trajectories may still contain ineffective, redundant, or risky steps. Directly using such trajectories for SFT can introduce noisy supervision and encourage models to imitate undesirable problem-solving behaviors. Therefore, we propose SWE-Prime, a multi-granularity, two-stage SFT data sele
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Dewu Zheng →
“SWE-Prime: Fewer Trajectories, Better Performance”
- LinkedLinked via arxiv author · 85%Ruizhe Ye →
“SWE-Prime: Fewer Trajectories, Better Performance”
- LinkedLinked via arxiv author · 85%Yanlin Wang →
“SWE-Prime: Fewer Trajectories, Better Performance”
- LinkedLinked via arxiv author · 85%Guangyang Ye →
“SWE-Prime: Fewer Trajectories, Better Performance”
- LinkedLinked via arxiv author · 85%Hongyu Zhang →
“SWE-Prime: Fewer Trajectories, Better Performance”