这篇论文让MLLMs学会用代码做数值推理，准确率涨了6个点，工具调用成功率超95%，代码开源可复现。
AIR方法通过扩展强化学习训练，使MLLMs具备自适应交错推理能力，专门处理代码增强的复杂数值计算任务。该方法包含两阶段冷启动数据构建、强化学习数据集筛选策略，以及基于群约束奖励函数的自适应工具调用策略。在评测基准上，平均性能提升6.1个百分点，其中交错推理样本准确率提高9.9个百分点，工具调用成功率超过95%。论文代码和数据集已开源。
AIR: Adaptive Interleaved Reasoning with Code in MLLMs
Following the paradigm shift initiated by OpenAI o3, interleaved reasoning with code to enhance multimodal large language models (MLLMs) has become a pivotal research frontier. The existing literature focuses primarily on tool-use within vision-perception tasks. However, such approaches typically rely on predefined heuristics for visual manipulation and are inherently incapable of addressing numerical computation problems due to their exclusive focus on visual operations. This paper empowers MLLMs with adaptive interleaved reasoning capabilities through extended reinforcement learning training on code-augmented complex numerical computation tasks. To this end, we propose a comprehensive three-component solution consisting of: a two-stage cold-start data construction pipeline, data filtering strategies for RL dataset curation, and an adaptive tool-invocation strategy leveraging a group-constrained reward function for interleaved reasoning trajectories. Extensive experiments demonstrate that after Reinforcement Learning training with the group-constrained reward function, performance improves by an average of 6.1 percentage points (pp) on evaluation benchmarks. Specifically, the accuracy for interleaved reasoning samples increases by 9.9 pp, and the overall success rate of tool-use exceeds 95%. Our data and code are available at: https://github.com/CongHan0808/AIR.git.