mlsanityData to 7 Oct 2026

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model?

arXiv 2602.03300 ↗First posted 3 Feb 2026cs.LG cs.AI cs.CLcited by 0

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

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Abstract (arXiv)

In this work, we aim to develop effective data synthesis techniques that autonomously synthesize multimodal training data for enhancing MLLMs in solving complex real-world tasks. To this end, we propose Collective Adversarial Data Synthesis (CADS), a novel and general approach to synthesize high-quality, diverse and challenging multimodal data for MLLMs. The core idea of CADS is to leverage collective intelligence to ensure high-quality and diverse generation, while exploring adversarial learning to synthesize challenging samples for effectively driving model improvement. Specifically, CADS operates with two cyclic phases, i.e., Collective Adversarial Data Generation (CAD-Generate) and Collective Adversarial Data Judgment (CAD-Judge). CAD-Generate leverages collective knowledge to jointly generate new and diverse multimodal data, while CAD-Judge collaboratively assesses the quality of synthesized data. In addition, CADS introduces an Adversarial Context Optimization mechanism to optimize the generation context to encourage challenging and high-value data generation. With CADS, we construct MMSynthetic-20K and train our model R1-SyntheticVL, which demonstrates superior performance on various benchmarks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 53 of its 67 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

53 of 67 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training28 Sep
  2. 2025Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis11 Mar
  3. 2025Valley2: Exploring Multimodal Models with Scalable Vision-Language Design10 Jan
  4. 2024LLaVA-OneVision: Easy Visual Task Transfer6 Aug
  5. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phone3 Aug
  6. 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs24 Jun
  7. 2024Dense Connector for MLLMs22 May
  8. 2023InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks21 Dec

Other references · 45

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  5. 2022Generative Adversarial Networkscited by 5,603
  6. 2025Qwen2.5-VL Technical Reportcited by 5,418
  7. 2024Qwen2.5 Technical Reportcited by 4,713
  8. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  9. 2023Qwen Technical Reportcited by 3,273
  10. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  11. 2023MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contextscited by 1,582
  12. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,193
33 more
  1. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,116
  2. 2023Vision-Language Models for Vision Tasks: A Surveycited by 834
  3. 2023Multimodal Chain-of-Thought Reasoning in Language Modelscited by 740
  4. 2025Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Modelscited by 653
  5. 2023WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instructcited by 618
  6. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 494
  7. 2025R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalizationcited by 364
  8. 2022Is synthetic data from generative models ready for image recognition?cited by 320
  9. 2025R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimizationcited by 296
  10. 2024Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Searchcited by 171
  11. 2023A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethicscited by 162
  12. 2025SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvementcited by 144
  13. 2025OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cyclescited by 122
  14. 2024Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Modelscited by 99
  15. 2024MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMscited by 72
  16. 2025Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thoughtcited by 69
  17. 2025Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learningcited by 68
  18. 2025R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPOcited by 52
  19. 2025Scaling Laws of Synthetic Data for Language Modelscited by 44
  20. 2025MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resourcescited by 37
  21. 2021Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signalscited by 35
  22. 2025GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Imagescited by 35
  23. 2025MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoningcited by 35
  24. 2024Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoningcited by 32
  25. 2024Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratchcited by 24
  26. 2024DataGen: Unified Synthetic Dataset Generation via Large Language Modelscited by 22
  27. 2025InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compressioncited by 21
  28. 2025Effective Training Data Synthesis for Improving MLLM Chart Understandingcited by 20
  29. 2025A Survey on Agentic Multimodal Large Language Modelscited by 19
  30. 2025TreeSynth: Synthesizing Diverse Data from Scratch via Tree-Guided Subspace Partitioningcited by 6
  31. 2025UniECG: Understanding and Generating ECG in One Unified Modelcited by 4
  32. 2025FLAMES: Improving LLM Math Reasoning via a Fine-Grained Analysis of the Data Synthesis Pipelinecited by 2
  33. 2025Syn-GRPO: Self-Evolving Data Synthesis for MLLM Perception Reasoningcited by 1

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