LLaVA-Critic-R1: Your Critic Model is Secretly a Strong Policy Model
Authors: name: Xiyao Wang, name: Chunyuan Li, name: Jianwei Yang, name: Kai Zhang, name: Bo Liu, name: Tianyi Xiong, name: Furong Huang

In vision-language modeling, critic models are typically trained to evaluate
outputs -- assigning scalar scores or pairwise preferences -- rather than to
generate responses. This separation from policy models, which produce the
responses, is so entrenched that critics are rarely considered for direct
policy use. In this work, we challenge this convention. We propose to
reorganize preference-labeled critic datasets into verifiable training signals
and perform reinforcement learning directly on a base generative model,
producing LLaVA-Critic-R1, a multimodal critic trained to optimize preference
judgments while retaining full generation ability. Surprisingly,
LLaVA-Critic-R1 emerges not only as a top-performing critic but also as a
competitive policy model -- matching or surpassing specialized reasoning VLMs
trained with in-domain data across 26 visual reasoning and understanding
benchmarks, with an average gain of +5.7% over its base model (Qwen-2.5-VL-7B).
Extending this approach to existing strong reasoning VLMs yields
LLaVA-Critic-R1+, which further advances policy performance without sacrificing
critic quality, achieving a SoTA performance of 71.9 on MMMU at the 7B scale.
Finally, we show that the enhanced critic ability benefits inference: applying
self-critique at test time yields an average +13.8% improvement on five
representative reasoning tasks without additional training. Our results reveal
that RL training on critic data can produce a unified model excelling at both
evaluation and generation, offering a simple path toward scalable,
self-improving multimodal systems.