EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards

Omkar Thawakar; Shravan Venkatraman; Ritesh Thawkar; Abdelrahman Shaker; Hisham Cholakkal; Rao Muhammad Anwer; Salman Khan; Fahad Khan

arXiv · 2511.16672

EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards

Omkar Thawakar·Shravan Venkatraman·Ritesh Thawkar·Abdelrahman Shaker·Hisham Cholakkal·Rao Muhammad Anwer·Salman Khan·Fahad Khan

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Abstract

Recent advances in large multimodal models (LMMs) have enabled impressive reasoning and perception abilities, yet most existing training pipelines still depend on human-curated data or externally verified reward models, limiting their autonomy and scalability. In this work, we strive to improve LMM reasoning capabilities in a purely unsupervised fashion (without any annotated data or reward distillation). To this end, we propose a self-evolving framework, named EvoLMM, that instantiates two cooperative agents from a single backbone model: a Proposer, which generates diverse, image-grounded questions, and a Solver, which solves them through internal consistency, where learning proceeds through a continuous self-rewarding process. This dynamic feedback encourages both the generation of informative queries and the refinement of structured reasoning without relying on ground-truth or human judgments. When using the popular Qwen2.5-VL as the base model, our EvoLMM yields consistent gains upto $\sim$3\% on multimodal math-reasoning benchmarks, including ChartQA, MathVista, and MathVision, using only raw training images. We hope our simple yet effective approach will serve as a solid baseline easing future research in self-improving LMMs in a fully-unsupervised fashion. Our code and models are available at https://github.com/mbzuai-oryx/EvoLMM.

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BibTeX RIS

Omkar Thawakar, Shravan Venkatraman, Ritesh Thawkar, Abdelrahman Shaker, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Fahad Khan. 2025-11-20. EvoLMM: Self-Evolving Large Multimodal Models with Continuous Rewards. https://arxiv.org/abs/2511.16672

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