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

2024-05-13 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

GenEvolve is a novel self-evolving framework designed for image generation agents, utilizing Tool-Orchestrated Visual Experience Distillation. This system models each generation attempt as a multi-turn trajectory, where an agent gathers evidence, selects visual references, invokes generation skills, and constructs a prompt-reference program. Unlike prior agentic methods relying on scalar image-level rewards, GenEvolve compares multiple trajectories for a single request, distilling best-worst differences into structured visual experience. This experience provides dense token-level supervision to a privileged teacher branch, enhancing the student agent's search, knowledge activation, and prompt construction. The framework introduces GenEvolve-Data and GenEvolve-Bench for training and evaluation. Experiments demonstrate GenEvolve's effectiveness, achieving a 0.5739 KScore with Nano Banana Pro and a 0.82 WiScore on the external WISE benchmark, surpassing strong direct generators like GPT-4o (0.80) and other agentic baselines.

Key takeaway

For AI Engineers developing advanced image generation systems, you should consider adopting agentic frameworks that learn from structured visual experience. GenEvolve demonstrates that distilling best-worst trajectory comparisons provides superior, fine-grained guidance compared to scalar rewards, significantly improving tool orchestration and prompt construction. This approach allows your agents to self-evolve, leading to higher-quality, more factually grounded, and visually coherent outputs for demanding open-ended requests.

Key insights

Self-evolving image generation agents learn from best-worst trajectory comparisons via visual experience distillation for improved tool orchestration.

Principles

• Image generation benefits from agentic planning.
• Trajectory comparison yields dense learning signals.
• Distilling visual experience improves tool orchestration.

Method

GenEvolve models generation as tool-orchestrated visual trajectories. It extracts best-worst differences into structured visual experience, then uses importance-weighted sampled-token reverse-KL distillation to provide token-level supervision to a student policy.

In practice

• Use multi-turn agentic planning for complex prompts.
• Implement best-worst trajectory comparison for feedback.
• Apply token-level distillation for fine-grained policy updates.

Topics

• Image Generation Agents
• Visual Experience Distillation
• Tool Orchestration
• Self-Evolving AI
• Multimodal LLMs
• Prompt Engineering
• Reinforcement Learning

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.