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AI Research
arXiv CS.AI·3d ago·5 min read
SWE-Critix: Post-Training Generalist Verifiers for Assessing Coding-Agent Trajectories
TL;DR
SWE-Critix is a post-training pipeline designed to create generalist verifiers for coding agents, improving their ability to assess and explain their execution trajectories. The approach enhances classification accuracy from 57% to 78% through a combination of supervised fine-tuning and reinforcement learning.
✦ Why It Matters
Implement SWE-Critix to enhance the evaluation of coding agents in your software projects today.
Key Takeaways
- Explore the SWE-Critix pipeline for coding agent evaluation.
- Utilize the released models and datasets for your own research.
- Consider integrating generalist verifiers into your software engineering tasks.
- Stay updated on advancements in coding agent verification techniques.
How It Works
SWE-Critix employs a two-step training process: first, it uses supervised fine-tuning to improve the model's ability to classify task completion, followed by reinforcement learning to refine its judgment and explanation capabilities. This combination allows the verifier to not only assess the success of coding agents but also articulate the reasoning behind its evaluations.