AI Research
Inducing Reward-Free Judging Rubrics that Reduce Over-Crediting in Agent Evaluation
Improving AI evaluation reliability by inducing evidence-based rubrics to catch agent failures.
Evaluating language-model agents at scale increasingly relies on a second language model as an automatic judge, because the gold signal, an executable environment reward, is expensive, slow, or unavailable at deployment time. Such a judge is a reward-free proxy whose value depend...
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ArXiv CS.AI
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ArXiv CS.AI
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By evolving human-readable rubrics, this approach significantly reduces the tendency of AI judges to incorrectly approve failing agent trajectories. This advancement is crucial for reliable deployment as it prioritizes identifying critical failures over simple statistical alignment.
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