WavReward is a novel reward feedback model designed to evaluate spoken dialogue systems by assessing both their intelligence quotient (IQ) and emotional quotient (EQ) through audio language models. It introduces a specialized evaluator using multi-sample feedback and reinforcement learning, along with the ChatReward-30K dataset, significantly outperforming existing evaluation models in accuracy and subjective testing across various spoken dialogue scenarios.

JudgeLRM introduces a novel approach to using Large Language Models (LLMs) as evaluators, particularly in complex reasoning tasks. By employing reinforcement learning with judge-wise rewards, JudgeLRM models significantly outperform traditional Supervised Fine-Tuning methods and current leading models, demonstrating superior performance in tasks that require deep reasoning.