Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale

TL;DR

This paper introduces a large-scale vision-centric dataset and synthesis framework for complex visual reasoning problems, significantly improving multimodal reasoning models' performance across various benchmarks and modalities.

Contribution

A novel two-stage synthesis framework creating over 1 million diverse visual problems, enhancing vision-centric reasoning models and enabling cross-modality transfer.

Findings

Finetuning Qwen2.5-VL-7B on the dataset outperforms existing open-data models.

The dataset improves reasoning in text-only and audio modalities.

High-quality data with reasoning traces is crucial for scaling online RL.

Abstract

Despite rapid progress, multimodal reasoning still lacks a systematic approach to synthesize large-scale vision-centric datasets beyond visual math. We introduce a framework able to synthesize vision-centric problems spanning diverse levels of complexity, and the resulting dataset with over 1M high-quality problems including: reasoning traces, preference data, and instruction prompts supporting SFT, offline and online RL. Our vision-centric synthesis framework uses a two-stage process focusing on: (1) generating diverse verifiable questions from existing images at scale, and (2) creating complex compositional visual problems by merging simpler questions. Remarkably, finetuning Qwen2.5-VL-7B on our data outperforms existing open-data baselines across evaluated vision-centric benchmarks, and our best configurations match or surpass strong closed-data models such as MiMo-VL-7B-RL on Vstar Bench, CV-Bench and MMStar-V. Notably, despite being entirely vision-centric, our data transfers positively to text-only reasoning (MMLU-Pro, +3.7%) and audio reasoning (MMAU, +1.32%), demonstrating its effectiveness. Similarly, despite containing no embodied visual data, we observe notable gains (NiEH, +8.8%) when evaluating open-ended embodied QA. Lastly, we use our data to comprehensively analyze at scale (1M+) the entire VLM post-training pipeline showing that (i) SFT on high-quality data with cognitive behaviors on reasoning traces is essential to scale online RL, (ii) offline RL could match online RL's performance while disaggregating compute demands, and, (iii) SFT on high quality data also improve out-of-domain, cross-modality transfer.