Real-world data analysis often requires joint reasoning over heterogeneous artifacts such as tables, charts, and textual reports. Training reliable multimodal large language model (MLLM) agents for this setting remains challenging, as large-scale aligned training data are scarce, and outcome-only rewards are too coarse to distinguish grounded cross-source analysis from spurious success. We introduce OmniDA, a training recipe that addresses these two challenges with a scalable multimodal data-synthesis pipeline and an Evidence-Aware Process Reward (EAPR) that supervises both evidence recovery and procedural validity. Through this pipeline, we construct OmniDA-11K, a high-quality training corpus for multimodal data analysis. When instantiated with Qwen3-VL-8B, OmniDA improves over the corresponding agentic baseline by 19.2 points on average across five challenging benchmarks and achieves the strongest overall performance among existing open-source data-analysis agents. These results highlight the value of supervising evidence recovery and analytical procedure beyond final-answer correctness.

Recently, large reasoning models (LRMs) have demonstrated state-of-the-art performance across a wide range of benchmarks. However, a common challenge for these models is the “overthinking” problem, which leads to excessive reasoning steps and significant computational overhead. Furthermore, the issues with long Chain-of-Thought (CoT) are especially pronounced in smaller models (≤ 3B parameters). Aside from producing excessively verbose “reflection words”, they often exhibit repetition and get trapped in unproductive generation loops. Existing solutions typically involve either using flexible reasoning chains as training data or leveraging the model’s latent space to bypass intermediate reasoning steps, but none of these methods have considered directly optimizing reasoning trajectories during the sampling phase of training. In our work, we introduce the Multi-Turn Intervention Sampling Framework (MuTIS). Our framework leverages multi-turn interventions to produce concise reasoning chains. It fine-tunes reasoning models through reinforcement learning, demonstrably breaking the accuracy-efficiency trade-off. It also demonstrates strong scalability, exhibiting excellent performance on 7B models. Code is available at https://github.com/Edric-Zhao/MuTIS/tree/main.