ReVisual‑R1: A New Open‑Source 7B Multimodal LLM with Deep, Thoughtful Reasoning
Researchers from Tsinghua University, Shanghai Jiao Tong University, and the Shanghai Artificial Intelligence Laboratory have released ReVisual‑R1, a pioneering 7 billion‑parameter multimodal large language model (MLLM) open‑sourced for public use. It offers advanced, context‑rich reasoning across both vision and text—unveiling new possibilities for explainable AI.
🧠 Why ReVisual‑R1 Matters
Training multimodal models to reason—not just perceive—poses a significant challenge. Previous efforts in multimodal chain‑of‑thought (CoT) reasoning were limited by training instability and superficial outputs. ReVisual‑R1 addresses these issues by blending text‑only and multimodal reinforcement learning (RL), yielding deeper and more accurate analysis.
🚀 Innovative Three‑Stage Training Pipeline
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Cold‑Start Pretraining (Text Only)
Leveraging carefully curated text datasets to build strong reasoning foundations that outperform many zero‑shot models, even before RL is applied. -
Multimodal RL with Prioritized Advantage Distillation (PAD)
Enhances visual–text reasoning through progressive RL, avoiding gradient stagnation typical in previous GRPO approaches. -
Final Text‑Only RL Refinement
Further improves reasoning fluency and depth, producing coherent and context‑aware multimodal outputs.
📚 The GRAMMAR Dataset: Key to Quality Reasoning
ReVisual‑R1 is trained on GRAMMAR, a meticulously curated dataset combining text and multimodal data. It offers nuanced reasoning tasks with coherent logic—unlike shallow, noisy alternatives—ensuring the model learns quality thinking patterns.
🏆 Benchmark‑Topping Performance
On nine out of ten benchmarks—including MathVerse, MathVision, WeMath, LogicVista, DynaMath, AIME 2024, and AIME 2025—ReVisual‑R1 outperforms open‑source peers and competes with commercial models, emerging as a top-performing open‑source 7B MLLM.
🔍 What This Means for AI Research
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Staged Training Works: Combining text-based pretraining with multimodal RL produces better reasoning than one-step methods.
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PAD Innovation: Stabilizes multimodal learning by focusing on high‑quality signals.
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Model Accessibility: At 7B parameters and fully open-source, ReVisual‑R1 drives multimodal AI research beyond large-scale labs.
✅ Final Takeaway
ReVisual‑R1 delivers long‑form, image‑grounded reasoning at the open‑source level—transforming the landscape for explainable AI. Its innovative training pipeline, multi-modal fluency, and benchmark dominance make it a new foundation for small, intelligent agents across education, robotics, and data analysis.
