Awesome_MLLMs_Reasoning

In this repository, we will continuously update the latest papers, projects, and other valuable resources that advance MLLM reasoning, making learning more efficient for everyone!

📢 Updates

• 2025.03: We released this repo. Feel free to open pull requests.

📚 Table of Contents

• Awesome_MLLMs_Reasoning

  • 1. Technical Report
  • 2. Generated Data Guided Post-Training
  • 3. Test-time Scaling
  • 4. Collaborative Reasoning
  • 5. MLLM Reward Model
  • 6. Benchmarks
  • 7. Applications
• Open-Source Projects

📖 Papers

📝 1.Technical Report

We also feature some well-known technical reports on Large Language Models (LLMs) reasoning.

• [2507] [GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning] (GLM-V Team) Technical Report

• [2506] [MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention] (MiniMax Team) Technical Report

• [2506] [MiMo-VL Technical Report] (LLM-Core Xiaomi) Technical Report

• [2504] [Kimi-VL Technical Report] (Kimi Team) Technical Report

• [2503] [Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought] (SkyWork AI) Technical Report Model

• [2503] [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs] (Microsoft) Technical Report

• [2503] [QwQ-32B: Embracing the Power of Reinforcement Learning](Qwen Team) Technical Report CodeModel

• [2501] [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](DeepSeek Team) Technical Report

• [2501] [Kimi k1.5: Scaling Reinforcement Learning with LLMs](Kimi Team) Technical Report

📌 2.Generated Data Guided Post-Training

Title	Date	Code/Huggingface	Taxonomy
Zebra-CoT: A Dataset for Interleaved Vision-Language Reasoning	2025.07	Github Huggingface	SFT
VTS-V: Multi-step Visual Reasoning with Visual Tokens Scaling and Verification	2025.06	Github Paper	SFT
Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing	2025.06	Paper	RL
SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis	2025.06	Paper	RL
Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning	2025.06	Github	RL
GRIT: Teaching MLLMs to Think with Images	2025.05	Paper	SFT
DeepEyes: Incentivizing “Thinking with Images” via Reinforcement Learning	2025.05	Paper	RL
Chain-of-Focus: Adaptive Visual Search and Zooming for Multimodal Reasoning via RL	2025.05	Paper	RL
SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement	2025.04	Paper	RL
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks	2025.03	Paper Code	RL
Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning	2025.03	Paper	RL
R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization	2025.03	Paper	RL
R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization	2025.03	Github Huggingface	RL
MM-EUREKA: Exploring the Frontiers of Multimodal Reasoning with Rule-Based Reinforcement Learning	2025.03	Github Huggingface	RL
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models	2025.03	Paper	RL
R1-Zero’s “Aha Moment” in Visual Reasoning on a 2B Non-SFT Model	2025.03	Paper Blog Code	RL
Visual-RFT: Visual Reinforcement Fine-Tuning	2025.03	Paper Code	RL
OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference	2025.02	Paper Code	RLHF
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification	2025.02	Paper Code	RL
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment	2025.02	Paper Code	RLHF
Can We Generate Images with CoT? Let’s Verify and Reinforce Image Generation Step by Step	2025.01	Paper Code	RL
Imagine while Reasoning in Space: Multimodal Visualization-of-Thought	2025.01	Paper	RL
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs	2025.01	Paper	RL
URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics	2025.01	Paper	RL
Technical Report on Slow Thinking with LLMs: Visual Reasoning	2025.01	Paper	RL
Progressive Multimodal Reasoning via Active Retrieval	2024.12	Paper	RL
Diving into Self-Evolving Training for Multimodal Reasoning	2024.12	Paper	RL
TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action	2024.12	Paper	RL
Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension	2024.12	Paper	RL
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale	2024.12	Paper	SFT
Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization	2024.11	Paper	RLHF
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning	2024.11	Paper	RL
Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models	2024.11	Paper	RL
AtomThink: A Slow Thinking Framework for Multimodal Mathematical Reasoning	2024.11	Paper	RL
LLaVA-o1: Let Vision Language Models Reason Step-by-Step	2024.11	Paper	SFT
Vision-Language Models Can Self-Improve Reasoning via Reflection	2024.11	Paper	RL
Improve Vision Language Model Chain-of-thought Reasoning	2024.10	Github Huggingface	SFT & RL
Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models	2024.03	Paper	SFT
Shikra: Unleashing Multimodal LLM’s Referential Dialogue Magic	2023.06	Paper	SFT
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🚀 3.Test-time Scaling

• [2502] [Boosting Multimodal Reasoning with MCTS-Automated Structured Thinking] (THU) Paper

• [2502] [MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs] (USC) Paper

• [2412] [Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension] (University of Maryland) Paper

• [2411] [Vision-Language Models Can Self-Improve Reasoning via Reflection] (NJU) Paper Code

• [2402] [Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models] (THU) Paper Code

• [2402] [V-STaR: Training Verifiers for Self-Taught Reasoners] (Mila, Universite de Montreal) Paper

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🚀 4.Collaborative Reasoning

This kind of method aims to use small models(tool or visual expert) or multiple MLLMs to do collaborative reasoning.

• [2412] [Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension] (University of Maryland) Paper

• [2410] [VipAct: Visual-Perception Enhancement via Specialized VLM Agent Collaboration and Tool-use] (Dartmouth College) Paper

• [2406] [Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models] (University of Washington) Paper

• [2409] [Visual Agents as Fast and Slow Thinkers] (UCLA) Paper

• [2312] [Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models] (Google Research) Paper

• [2211] [Visual Programming: Compositional visual reasoning without training] (Allen Institute for AI) Paper

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💰 5.MLLM Reward Model

• [2505] R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning Paper

• [2503] [VisualPRM: An Effective Process Reward Model for Multimodal Reasoning](Shanghai AI Lab) Paper Blog

• [2503] [Unified Reward Model for Multimodal Understanding and Generation] (Shanghai AI Lab) Paper

• [2502] [Preference VLM: Leveraging VLMs for Scalable Preference-Based Reinforcement Learning] (University of California, Riverside) Paper

• [2501] [InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model] (Shanghai AI Lab) Paper

• [2410] [TLDR: Token-Level Detective Reward Model for Large Vision Language Models] (Meta) Paper

• [2410] [FINE-GRAINED VERIFIERS: PREFERENCE MODELING AS NEXT-TOKEN PREDICTION IN VISION-LANGUAGE ALIGNMENT] (NUS) Paper

• [2410] [LLaVA-Critic: Learning to Evaluate Multimodal Models] (ByteDance) Paper Code

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📊 6.Benchmarks

Benchmark Papers:

• [2503] [CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation] (CMU) Paper

• [2503] [reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs] (Meta) Paper

• [2503] [How Do Multimodal Large Language Models Handle Complex Multimodal Reasoning? Placing Them in An Extensible Escape Game] (THU) Paper

• [2502] [Multimodal RewardBench: Holistic Evaluation of Reward Models for Vision Language Models] (FAIR) Paper Code

• [2502] [ZeroBench: An Impossible* Visual Benchmark for Contemporary Large Multimodal Models] (University of Cambridge) Paper Code

• [2502] [MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models] (Tencent Hunyuan Team) Paper Code

• [2502] [MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency] (CUHK MMLab) Paper Code

• [2410] [HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks] (CityU HK) Paper Homepage

• [2406] [(CV-Bench)Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs] (NYU) Paper Code

• [2404] [BLINK: Multimodal Large Language Models Can See but Not Perceive] (University of Pennsylvania) Paper

• [2401] [(MMVP) Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs] (NYU) Paper

• [2312] [(V∗Bench) V∗: Guided Visual Search as a Core Mechanism in Multimodal LLMs] (UCSD) Paper

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📦 7.Applications

• [2503] [Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models] (Emory University) Paper

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🛠️ Open-Source Projects

• [MetaSpatial: Reinforcing 3D Spatial Reasoning in VLMs for the Metaverse] Code

• [R1-Omni: Explainable Omni-Multimodal Emotion Recognition with Reinforcement Learning] Code

• [R1-V: Reinforcing Super Generalization Ability in Vision Language Models with Less Than $3] Code Report

• [EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework] Code

• [R1-Onevision：An Open-Source Multimodal Large Language Model Capable of Deep Reasoning] Paper Code

• [LMM-R1] Code Paper

• [VLM-R1: A stable and generalizable R1-style Large Vision-Language Model] Code

• [Multi-modal Open R1] Code

• [Video-R1: Towards Super Reasoning Ability in Video Understanding] Code

• [Open-R1-Video] Code

• [R1-Vision: Let's first take a look at the image] Code

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🤝 Contributing

You’re welcome to submit new resources or paper links. Please initiate a Pull Request directly.