Search papers, labs, and topics across Lattice.
ToolArtist introduces a fully agentic image generation model that integrates reasoning, tool invocation, and image generation into a single unified policy, overcoming limitations of existing text-to-image models. By employing a novel Reinforcement Learning approach called Reason-Act-Draw GRPO (RAD-GRPO), the model optimizes for both intent and quality rewards, leading to superior performance on open-world tasks. The results demonstrate that a cohesive agent policy significantly outperforms traditional fixed workflows and partially controlled systems in generating complex images.
ToolArtist achieves unprecedented synergy in image generation by unifying reasoning and tool use under a single agent policy, outperforming conventional methods.
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.
