Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge
Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.
69b373452ad6c5d4cf4869ec
Junjie Wu
69b373452ad6c5d4cf4869ed
Xuan Kan
69b373452ad6c5d4cf4869ee
Zihao He
69b373452ad6c5d4cf4869ef
Shunwen Tan
69b373452ad6c5d4cf4869f0
Bo Pan
69b373452ad6c5d4cf4869f1
Kaitai Zhang
Multi-Task Reinforcement Learning for Enhanced Multimodal LLM-as-a-Judge
Multimodal Large Language Models (MLLMs) have been widely adopted as MLLM-as-a-Judges due to their strong alignment with human judgment across various visual tasks. However, most existing judge models are optimized for single-task scenarios and struggle to generalize to diverse contexts, which is a critical requirement for reliable evaluation. To address this limitation, we propose Multi-Task Reinforcement Learning for MLLM-as-a-Judge (MT-RL-Judge), a framework that jointly optimizes the judge model across multiple tasks, leveraging the generalization capabilities of RL. Experimental results against several strong baselines demonstrate that MT-RL-Judge outperforms strong baselines in both judgment consistency and correlation with human preferences. Furthermore, our approach exhibits robust generalization on out-of-distribution tasks, further validating its effectiveness.
Multi-Task Reinforcement Learning framework improves multimodal large language models' judgment consistency and generalization across diverse visual tasks.
Multimodal Large Language Models MLLM-as-a-Judge reinforcement learning multi-task optimization judgment consistency human preference alignment out-of-distribution generalization