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

Explainability methods need practical validation beyond current standards: The paper argues that existing ways of evaluating explainability in AI systems—such as measuring how faithful or compact explanations are—don't necessarily reflect whether these explanations actually help people understand and fix problems. This suggests the field may be optimizing for the wrong metrics.

A new benchmark tests explainability through real debugging tasks: EvalXRL proposes evaluating explainability methods by their ability to help diagnose and repair malfunctioning AI agents, using an LLM coding agent to attempt fixes based on different explanation methods. This grounds evaluation in a concrete, practical outcome rather than abstract measures.

Interactive, iterative explanation use matters: The benchmark emphasizes that effective explanations should support a closed-loop process where users form hypotheses, test them, and refine their understanding—similar to the scientific method. This suggests that one-time explanations may be insufficient for responsible AI debugging.

Head-to-head comparison reveals performance differences: By comparing multiple XRL methods directly on the same debugging tasks, the paper enables the first systematic evaluation of which explanation approaches actually work best in practice, rather than assuming all methods are equally useful.

Practical debugging success is the ultimate measure: Using the actual performance improvement of repaired agents as the final score prioritizes real-world impact over theoretical properties, aligning explainability research with the practical goal of making AI systems more reliable and trustworthy.