MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data
Self-evolving has emerged as a key paradigm for improving foundational models such as Large Language Models (LLMs) and Vision Language Models (VLMs) with minimal human intervention. While recent approaches have demonstrated that LLM agents can self-evolve from scratch with little to no data, VLMs introduce an additional visual modality that typically requires at least some seed data, such as images, to bootstrap the self-evolution process. In this work, we present Multi-model Multimodal Zero (MM-Zero), the first RL-based framework to achieve zero-data self-evolution for VLM reasoning. Moving beyond prior dual-role (Proposer and Solver) setups, MM-Zero introduces a multi-role self-evolving training framework comprising three specialized roles: a Proposer that generates abstract visual concepts and formulates questions; a Coder that translates these concepts into executable code (e.g., Python, SVG) to render visual images; and a Solver that performs multimodal reasoning over the generated visual content. All three roles are initialized from the same base model and trained using Group Relative Policy Optimization (GRPO), with carefully designed reward mechanisms that integrate execution feedback, visual verification, and difficulty balancing. Our experiments show that MM-Zero improves VLM reasoning performance across a wide range of multimodal benchmarks. MM-Zero establishes a scalable path toward self-evolving multi-model systems for multimodal models, extending the frontier of self-improvement beyond the conventional two-model paradigm.
69b0d4a71f7902d47f6df9c9
Zongxia Li
admin_assigned
2026-03-11T08:55:40.987Z
642447e873f7a0d40b30d677
https://cdn-avatars.huggingface.co/v1/production/uploads/642447e873f7a0d40b30d677/hbUnfIhKZCeSSPv9wGAzk.png
LZX
zli12321
user
zli12321
69b0d4a71f7902d47f6df9ca
Hongyang Du
admin_assigned
2026-03-11T08:55:54.051Z
667230de876f9f27d6b097ea
https://cdn-avatars.huggingface.co/v1/production/uploads/667230de876f9f27d6b097ea/hhz4nfTUdAAMIPXw3A342.jpeg
Hongyang Du
Hongyang-Du
user
Hongyang-Du
69b0d4a71f7902d47f6df9cb
Chengsong Huang
admin_assigned
2026-03-11T08:56:12.467Z
62ea79dd01ed9b0e8f61ccd3
/avatars/70af83e0e267be39fcd5f23b85e2dafa.svg
Chengsong Huang
ChengsongHuang
user
ChengsongHuang
69b0d4a71f7902d47f6df9cc
Xiyang Wu
admin_assigned
2026-03-11T08:56:21.012Z
64ea62f918d79efd533c93fe
/avatars/9985a789ce11b788de2cba12adfb72fc.svg
Xiyang Wu
wuxiyang
user
wuxiyang
69b0d4a71f7902d47f6df9cd
Lantao Yu
admin_assigned
2026-03-11T08:56:27.549Z
6594d3d6c27d210c3e3253f8
/avatars/ea64682a28670a651893e4ed72f817ba.svg
Lantao Yu
complexfilter
user
complexfilter
69b0d4a71f7902d47f6df9ce
Yicheng He
69b0d4a71f7902d47f6df9cf
Jing Xie
69b0d4a71f7902d47f6df9d0
Xiaomin Wu
69b0d4a71f7902d47f6df9d1
Zhichao Liu
claimed_verified
2026-03-12T09:07:53.074Z
65278fbbf39fb9033710dd66
/avatars/1fe6c996a15dc7f87828f5d912107b75.svg
Zhichao Liu
liuzceecs
user
liuzceecs
69b0d4a71f7902d47f6df9d2
Jiarui Zhang
claimed_verified
2026-03-17T17:34:57.541Z
635b99d47a1656011516bff9
/avatars/7243c4171ff127ba90631f105881d9d7.svg
jiarui zhang
jrzhang
user
jrzhang
69b0d4a71f7902d47f6df9d3
Fuxiao Liu
admin_assigned
2026-03-11T08:57:01.311Z
64618b9496259bec21d44704
https://cdn-avatars.huggingface.co/v1/production/uploads/64618b9496259bec21d44704/FCzN0p7Elh8MeUTuM-hya.jpeg
Fuxiao Liu
Fuxiao
user
Fuxiao
MM-Zero: Self-Evolving Multi-Model Vision Language Models From Zero Data
Self-evolving has emerged as a key paradigm for improving foundational models such as Large Language Models (LLMs) and Vision Language Models (VLMs) with minimal human intervention. While recent approaches have demonstrated that LLM agents can self-evolve from scratch with little to no data, VLMs introduce an additional visual modality that typically requires at least some seed data, such as images, to bootstrap the self-evolution process. In this work, we present Multi-model Multimodal Zero (MM-Zero), the first RL-based framework to achieve zero-data self-evolution for VLM reasoning. Moving beyond prior dual-role (Proposer and Solver) setups, MM-Zero introduces a multi-role self-evolving training framework comprising three specialized roles: a Proposer that generates abstract visual concepts and formulates questions; a Coder that translates these concepts into executable code (e.g., Python, SVG) to render visual images; and a Solver that performs multimodal reasoning over the generated visual content. All three roles are initialized from the same base model and trained using Group Relative Policy Optimization (GRPO), with carefully designed reward mechanisms that integrate execution feedback, visual verification, and difficulty balancing. Our experiments show that MM-Zero improves VLM reasoning performance across a wide range of multimodal benchmarks. MM-Zero establishes a scalable path toward self-evolving multi-model systems for multimodal models, extending the frontier of self-improvement beyond the conventional two-model paradigm.
MM-Zero enables zero-data self-evolution of vision-language models through a multi-role framework with proposer, coder, and solver components trained via group relative policy optimization.
self-evolving Large Language Models Vision Language Models reinforcement learning multimodal reasoning Group Relative Policy Optimization visual concepts executable code visual verification difficulty balancing