Paper page - GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation

Source: https://huggingface.co/papers/2605.21605

Abstract

A self-evolving image generation framework uses tool-orchestrated trajectories and visual experience distillation to improve generative capabilities through iterative learning and reference-based prompting.

Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model’s internal generative ability with external resources. As requests become more diverse and demanding, we aim to develop a generalimage-generation agentthat can self-evolve through trajectories and use tools more effectively across varied generation challenges. To this end, we propose GenEvolve, aself-evolving frameworkbased onTool-Orchestrated Visual Experience Distillation. In GenEvolve, each generation attempt is modeled as atool-orchestrated trajectory, where the agent gathers evidence, selects references, invokes generation skills, and composes them into a prompt-reference program. Unlike existing agentic generation methods that mainly rely on image-level scalar rewards, GenEvolve compares multiple trajectories for the same request and abstracts best-worst differences into structured visual experience, provided only to a privileged teacher branch. Inspired byon-policy self-distillation,Visual Experience Distillationprovides dense token-level supervision, helping the student internalize better search, knowledge activation,reference selection, andprompt construction. We further construct GenEvolve-Data and GenEvolve-Bench. Experiments on public benchmarks and GenEvolve-Bench show substantial gains over strong baselines, achieving state-of-the-art performance among current image-generation frameworks. Our website is as follows: https://ephemeral182.github.io/GenEvolve/

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