Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotated supervision. Recent self-rewarding approaches attempt to overcome this constraint by allowing models to act as their own critics or reward providers. Yet, purely text-based self-evaluation struggles to verify complex visual reasoning steps and often suffers from evaluation hallucinations. To address these challenges, inspired by recent advances in tool-integrated reasoning, we propose Agent0-VL, a self-evolving vision-language agent that achieves continual improvement with tool-integrated reasoning. Agent0-VL incorporates tool usage not only into reasoning but also into self-evaluation and self-repair, enabling the model to introspect, verify, and refine its reasoning through evidence-grounded analysis. It unifies two synergistic roles within a single LVLM: a Solver that performs multi-turn tool-integrated reasoning, and a Verifier that generates structured feedback and fine-grained self-rewards through tool-grounded critique. These roles interact through a Self-Evolving Reasoning Cycle, where tool-based verification and reinforcement learning jointly align the reasoning and evaluation distributions for stable self-improvement. Through this zero-external-reward evolution, Agent0-VL aligns its reasoning and verification behaviors without any human annotation or external reward models, achieving continual self-improvement. Experiments on geometric problem solving and visual scientific analysis show that Agent0-VL achieves an 12.5% improvement over the base model. Our code is available at https://github.com/aiming-lab/Agent0/Agent0-VL{this https URL}.
6926877a243b2216fb75caca
Jiaqi Liu
claimed_verified
2025-11-26T09:27:22.964Z
684ff37fa383bc5d6b0ff77f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/0JPr-cd_rxQz3k6rmzBOF.png
JiaqiLiu
JiaaqiLiu
user
JiaaqiLiu
6926877a243b2216fb75cacb
Kaiwen Xiong
6926877a243b2216fb75cacc
Peng Xia
claimed_verified
2025-11-27T09:59:53.380Z
643e9ee6f6bb3c31a26e7bc4
/avatars/acfaa7d6a23dada24c86b954c3be116a.svg
Peng Xia
richardxp888
user
richardxp888
6926877a243b2216fb75cacd
Yiyang Zhou
6926877a243b2216fb75cace
Haonian Ji
6926877a243b2216fb75cacf
Lu Feng
6926877a243b2216fb75cad0
Siwei Han
6926877a243b2216fb75cad1
Mingyu Ding
6926877a243b2216fb75cad2
Huaxiu Yao
Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
Vision-language agents have achieved remarkable progress in a variety of multimodal reasoning tasks; however, their learning remains constrained by the limitations of human-annotated supervision. Recent self-rewarding approaches attempt to overcome this constraint by allowing models to act as their own critics or reward providers. Yet, purely text-based self-evaluation struggles to verify complex visual reasoning steps and often suffers from evaluation hallucinations. To address these challenges, inspired by recent advances in tool-integrated reasoning, we propose Agent0-VL, a self-evolving vision-language agent that achieves continual improvement with tool-integrated reasoning. Agent0-VL incorporates tool usage not only into reasoning but also into self-evaluation and self-repair, enabling the model to introspect, verify, and refine its reasoning through evidence-grounded analysis. It unifies two synergistic roles within a single LVLM: a Solver that performs multi-turn tool-integrated reasoning, and a Verifier that generates structured feedback and fine-grained self-rewards through tool-grounded critique. These roles interact through a Self-Evolving Reasoning Cycle, where tool-based verification and reinforcement learning jointly align the reasoning and evaluation distributions for stable self-improvement. Through this zero-external-reward evolution, Agent0-VL aligns its reasoning and verification behaviors without any human annotation or external reward models, achieving continual self-improvement. Experiments on geometric problem solving and visual scientific analysis show that Agent0-VL achieves an 12.5% improvement over the base model. Our code is available at https://github.com/aiming-lab/Agent0/Agent0-VL{this https URL}.
Agent0-VL, a self-evolving vision-language agent, incorporates tool usage into both reasoning and self-evaluation, enabling continual improvement through evidence-grounded analysis and reinforcement learning.
self-rewarding approaches tool-integrated reasoning self-evaluation self-repair introspect evidence-grounded analysis Solver Verifier Self-Evolving Reasoning Cycle reinforcement learning geometric problem solving visual scientific analysis