Agent0-VL: 探索自我进化代理用于工具集成的视觉-语言推理
Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning
AI总结：
Agent0-VL通过工具集成推理实现自我进化，无需人类标注或外部奖励，提升视觉-语言推理能力。
摘要：
视觉-语言代理在多种多模态推理任务中取得了显著进展；然而，其学习仍然受到人类标注监督的限制。最近的自我奖励方法试图通过允许模型充当自身的批评者或奖励提供者来克服这一限制。然而，纯粹基于文本的自我评估难以验证复杂的视觉推理步骤，且常遭受评估幻觉的问题。为了解决这些挑战，受最近工具集成推理进展的启发，我们提出了Agent0-VL，一种能够通过工具集成推理实现持续改进的自我进化视觉-语言代理。Agent0-VL不仅将工具使用融入推理，还融入自我评估和自我修复，使模型能够通过证据支持的分析进行自我反省、验证和修正推理。它将两种协同作用的角色整合到单一的LVLM中：一个执行多轮工具集成推理的求解器，以及一个通过工具支持的批评生成结构化反馈和细粒度自我奖励的验证器。这些角色通过自我进化推理循环相互作用，其中基于工具的验证和强化学习共同对推理和评估分布进行对齐，以实现稳定的自我改进。通过这种零外部奖励的进化，Agent0-VL在没有人类标注或外部奖励模型的情况下对齐其推理和验证行为，实现持续的自我改进。在几何问题解决和视觉科学分析上的实验表明，Agent0-VL相比基线模型实现了12.5%的提升。我们的代码可在https://github.com/aiming-lab/Agent0上获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
