ReasonUni: Reasoning-Augmented Reconstruction Advances Unified Multimodal Models

ReasonUni introduces a reasoning-augmented reconstructive post‑training framework for unified multimodal models (UMMs) that couples image‑to‑text understanding and text‑to‑image generation. The framework first reasons about the desired edit and its target state, then generates the target image conditioned on this reasoning output. A tree‑structured reinforcement learning method, TreeGRPO, jointly optimizes the reasoning and generation modules, yielding consistent performance gains across multiple benchmarks.

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• diffusion
• reinforcement-learning
• multimodal
• benchmarks