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AI Research
arXiv CS.AI·1d ago·5 min read
Beyond the Verdict: Agentic Code Judging with Verifiable Feedback
TL;DR
ARBITER, a 35B mixture-of-experts verifier, enhances code judging by providing counterexamples and feedback, achieving a mean verdict accuracy of 87.41%. This model significantly improves code selection and refinement, helping to elevate scores in competitive programming.
✦ Why It Matters
Implement ARBITER to enhance your code evaluation processes and improve competitive programming outcomes.
Key Takeaways
- Utilize ARBITER for effective code verification and feedback.
- Incorporate counterexamples to improve debugging processes.
- Leverage ARBITER's capabilities to enhance competitive programming scores.
- Explore reinforcement learning techniques for model training.
How It Works
ARBITER operates by analyzing the problem statement and source code of candidate programs, generating counterexamples to identify failures. It uses a mixture-of-experts architecture, allowing it to specialize in different aspects of code verification.
The model is trained in a controlled execution environment, which helps it learn to provide accurate feedback and improve its judgment capabilities.