adaptive-search

IncentRL presents a framework for preference-based reward shaping in reinforcement learning that uses KL divergence penalty to balance preference guidance with task performance, supported by theoretical analysis and experiments on MiniGrid benchmarks.

The paper proposes Evaluation-as-Search (EaS), an adaptive methodology for evaluating grounding failures in LLM-powered meeting assistants, and introduces MeetingProbe, a benchmark of over 3,000 annotated question-answer pairs to improve failure detection.