# Walk the Talk: Bridging the Reasoning-Action Gap for Thinking with Images via Multimodal Agentic Policy Optimization Source: [Walk the Talk: Bridging the Reasoning-Action Gap for Thinking with Images via Multimodal Agentic Policy Optimization]() Collection index: [llms.txt]() Data updated: 2026-04-10T05:00:04.898Z Authors: Wenhao Yang, Yu Xia, Jinlong Huang, Shiyin Lu, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Yuchen Zhou, Xiaobo Xia, Yuanyu Wan, Lijun Zhang, Tat-Seng Chua Published: 2026-04-08T07:48:07.000Z ## Abstract Recent advancements in Multimodal Large Language Models (MLLMs) have incentivized models to \`\`think with images'' by actively invoking visual tools during multi-turn reasoning. The common Reinforcement Learning (RL) practice of relying on outcome-based rewards ignores the fact that textual plausibility often masks executive failure, meaning that models may exhibit intuitive textual reasoning while executing imprecise or irrelevant visual actions within their agentic reasoning trajectories. This reasoning-action discrepancy introduces noise that accumulates throughout the multi-turn reasoning process, severely degrading the model's multimodal reasoning capabilities and potentially leading to training collapse. In this paper, we introduce Multimodal Agentic Policy Optimization (MAPO), bridging the gap between textual reasoning and visual actions generated by models within their Multimodal Chain-of-Thought (MCoT). Specifically, MAPO mandates the model to generate explicit textual descriptions for the visual content obtained via tool usage. We then employ a novel advantage estimation that couples the semantic alignment between these descriptions and the actual observations with the task reward. Theoretical findings are provided to justify the rationale behind MAPO, which inherently reduces the variance of gradients, and extensive experiments demonstrate that our method achieves superior performance across multiple visual reasoning benchmarks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [High-Resolution Visual Reasoning on HR-Bench (4K/8K configurations)](): High-Resolution Visual Reasoning; HR-Bench; Score (4K): 81. - [Fine-grained Visual Reasoning on V\* (Avg@8)](): Fine-grained Visual Reasoning; V\*; Avg@8 Overall: 89.5. - [Fine-grained visual reasoning on MME Realworld Lite](): Fine-grained Visual Reasoning; MME-RealWorld-Lite; Avg@1: 55.8. ## Original sources and implementation [Original paper]() [PDF]()