CR³: Boosting Compositional Reasoning in MLLMs Through Rule-Based Reinforcement Learning

Compositional reasoning is a critical capability for multimodal models, enabling systematic understanding of complex scenes through structured combinations of objects, attributes, and relations. However, existing research on this ability primarily focuses on vision-language models (VLMs, e.g., CLIP and SigLIP), with limited exploration of multimodal large language models (MLLMs). To address this gap, we introduce CR 3 , a novel framework that enhances compositional reasoning abilities of MLLMs via rule-based reinforcement learning. CR 3 leverages rule-based rewards to optimize the MLLM's policy on systematically curated multimodal instruction-following tasks, guided by a modeladaptive dynamic task mixing strategy. Our approach boosts performance by over 19% on three compositional reasoning benchmarks, significantly outperforming supervised finetuning (SFT) by at least 12%. Crucially, CR 3 demonstrates superior generalization by improving performance on out-ofdomain benchmarks where SFT methods degrade, highlighting its effectiveness and data efficiency.