🤖AI Summary
Researchers propose a new framework called Critic Rubrics to bridge the gap between academic coding agent benchmarks and real-world applications. The system learns from sparse, noisy human interaction data using 24 behavioral features and shows significant improvements in code generation tasks including 15.9% better reranking performance on SWE-bench.
Key Takeaways
- →Academic coding agent benchmarks don't reflect real-world conditions where human feedback is sparse and noisy.
- →Critic Rubrics framework uses 24 behavioral features derived from human-agent interaction traces to train better reward models.
- →The approach achieved 15.9% improvement in best-of-N reranking on SWE-bench coding benchmarks.
- →The system enables early stopping with 83% fewer attempts while maintaining performance.
- →The critic model can be used for both reinforcement learning training and inference-time scaling.
#ai-research#coding-agents#machine-learning#reinforcement-learning#human-feedback#arxiv#swe-bench#critic-models#semi-supervised#reward-modeling
Read Original →via arXiv – CS AI
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