Reinforced Attention Learning

Post-training via Reinforcement Learning (RL) has catalyzed significant reasoning gains in Large Language Models (LLMs) by scaling test-time computation. While recent efforts attempt to extend this paradigm to Multimodal Large Language Models (MLLMs) by incentivizing verbose rationales, we demonstrate that such gains are marginal for perception; indeed, excessive verbalization can inadvertently degrade performance. We propose Reinforced Attention Learning (RAL), a policy-gradient framework that treats internal attention distributions as an optimization target. Unlike conventional RL that focuses on high-reward token sequences, RAL explicitly explores and rewards optimal information allocation patterns during the generation process. By shifting optimization from "what to generate" to "where to attend", RAL enhances the model's ability to isolate salient signals within complex multimodal contexts. Extensive experiments across diverse image and video benchmarks show that RAL consistently outperforms standard GRPO and other baselines. We further extend this approach to On-Policy Attention Distillation, proving that transferring latent attention behaviors to student models yields superior cross-modal alignment compared to standard knowledge distillation. Our results establish attention policies as a principled, generic alternative for multimodal post-training. We will release our code and data upon publication.