ToolArtist: Tool-Using Unified Multimodal Models for Agentic Image Generation

2026-08-05

AI Summary

This work addresses the limitations of current text-to-image models in open-world scenarios, where they struggle to jointly handle complex semantic understanding, multi-step reasoning, and integration of external knowledge, while lacking a unified agent to coordinate reasoning, tool invocation, and generation. The authors propose a Unified Multimodal Model (UMM) post-training framework that, for the first time, encapsulates the entire image generation pipeline under a single agent policy. They introduce a Reason-Act-Draw GRPO reinforcement learning algorithm, supported by dedicated training infrastructure, combining supervised fine-tuning with reinforcement learning to orchestrate retrieval and generative tools. Their approach incorporates trajectory format conversion and a joint intent–quality reward mechanism. Experiments demonstrate substantial improvements over fixed-pipeline or partial-agent baselines, and the authors release both the training data and the complete post-training framework.

Abstract

Text-to-image (T2I) models can produce visually compelling images, yet they remain limited on open-world tasks that require complex semantic understanding, multi-step reasoning, and the integration of external world knowledge. Existing efforts introduce agent capabilities into image generation, but they either prescribe a fixed workflow or place only a subset of the open-world image generation process under agent control. Consequently, reasoning, tool invocation, and image generation are not coordinated by a single policy. We propose ToolArtist, a fully agentic image generation model obtained by post-training a Unified Multimodal Model (UMM). ToolArtist dynamically orchestrates reasoning, external tool use, and native image generation within one unified policy. During Supervised Fine-Tuning (SFT), we equip a teacher agent with search tools alongside an image-generation tool. We then convert the collected trajectories into a UMM compatible format, where the image-generation tool is concealed while the resulting generated images are retained. During Reinforcement Learning (RL), we develop an agentic RL infrastructure for UMMs and introduce Reason-Act-Draw GRPO (RAD-GRPO), which uses complementary intent and quality rewards to jointly optimize the model. Experiments show that placing the entire open-world image-generation process under an agent policy consistently outperforms approaches with fixed pipelines or only partially agent-controlled components. We release the training data and the complete post-training infrastructure.

Problem

• text-to-image generation
• open-world tasks
• agentic control
• multimodal reasoning
• tool use

Innovation

• ToolArtist
• Unified Multimodal Model
• Agentic Image Generation
• RAD-GRPO
• Reinforcement Learning