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Yotam Amitai

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#machine learning Preprint Aug 2026

Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents

EvalXRL is a benchmark in which a Large Language Model (LLM) coding agent uses different XRL methods to diagnose a held-out malfunction in an RL agent, and then repair it, and proposes the first head-to-head comparison of multiple XRL methods in closed-loop usage.

Ram Rachum, Yotam Amitai, Bálint Gyevnár et al. · 0 citations