SWE-Gym lets AI agents practice coding on real GitHub issues.
SWE-Gym introduces a training environment for software engineering agents, combining real GitHub tasks with executable test verification to improve automated code fixes.
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https://arxiv.org/abs/2412.21139
Original Problem 🤔:
→ Current software engineering automation relies heavily on proprietary models and lacks proper training environments with real-world tasks and verification mechanisms
→ Existing datasets either lack executable environments or use synthetic tasks, making it difficult to train effective agents
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Solution in this Paper 🛠️:
→ SWE-Gym provides 2,438 Python tasks from 11 popular repositories, each with complete codebase, runtime environment, and unit tests
→ Uses rejection sampling fine-tuning to train LLM agents on successful task completions
→ Implements verifier models trained on agent trajectories to enable better solution selection
→ Combines both general-purpose prompting and specialized workflow approaches
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Key Insights 💡:
→ Training environment quality matters more than quantity for real-world tasks
→ Performance scales consistently with more compute in both training and inference
→ Verifier models enable effective trajectory selection for better results
→ Combined approach of fine-tuned agents and verifiers achieves state-of-the-art performance
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Results 📊:
→ Achieved 19% absolute gains in resolve rate on SWE-Bench test sets
→ Reached 32% success rate on SWE-Bench Verified
→ Improved to 26% on SWE-Bench Lite
→ Demonstrated continuous scaling benefits with increased compute
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