18 citations · 28 across the 14 of their papers we have counts for
3 papers · 1 filter
Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents
Ram Rachum, Yotam Amitai, Bálint Gyevnár +2
This preliminary paper outlines a planned evaluation benchmark for Explainable Reinforcement Learning (XRL) methods. Current evaluations rely on functionally-grounded metrics like…
BXRL: Behavior-Explainable Reinforcement Learning
Ram Rachum, Yotam Amitai, Yonatan Nakar +2
A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learnin…
Truthfulness Despite Weak Supervision: Evaluating and Training LLMs Using Peer Prediction
Tianyi Alex Qiu, Micah Carroll, Cameron Allen
The evaluation and post-training of large language models (LLMs) rely on supervision, but strong supervision for difficult tasks is often unavailable, especially when evaluating fr…