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[2602.04884] Reinforced Attention Learning
Summary
The paper introduces Reinforced Attention Learning (RAL), a novel framework that optimizes internal attention distributions in multimodal large language models, enhancing their performance in complex tasks.
Why It Matters
As multimodal large language models become increasingly prevalent, optimizing their attention mechanisms is crucial for improving their reasoning and perception capabilities. RAL offers a promising approach to enhance model performance by focusing on where to attend rather than solely on output generation.
Key Takeaways
- RAL optimizes internal attention distributions instead of output sequences.
- The framework leads to improved grounding in complex multimodal inputs.
- Experiments show consistent performance gains over existing baselines.
- On-Policy Attention Distillation enhances cross-modal alignment.
- RAL positions attention policies as a viable alternative for multimodal post-training.
Computer Science > Computation and Language arXiv:2602.04884 (cs) [Submitted on 4 Feb 2026 (v1), last revised 12 Feb 2026 (this version, v2)] Title:Reinforced Attention Learning Authors:Bangzheng Li, Jianmo Ni, Chen Qu, Ian Miao, Liu Yang, Xingyu Fu, Muhao Chen, Derek Zhiyuan Cheng View a PDF of the paper titled Reinforced Attention Learning, by Bangzheng Li and 7 other authors View PDF HTML (experimental) Abstract:Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multimodal LLMs (MLLMs) through verbose rationales yields limited gains for perception and can even degrade performance. We propose Reinforced Attention Learning (RAL), a policy-gradient framework that directly optimizes internal attention distributions rather than output token sequences. By shifting optimization from what to generate to where to attend, RAL promotes effective information allocation and improved grounding in complex multimodal inputs. Experiments across diverse image and video benchmarks show consistent gains over GRPO and other baselines. We further introduce On-Policy Attention Distillation, demonstrating that transferring latent attention behaviors yields stronger cross-modal alignment than standard knowledge distillation. Our results position attention policies as a principled and general alternative for multimodal post-training. Subjects: Computation and Language (cs.C...
