GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation
  with Reinforcement Learning
Authors: name: Chengqi Duan, name: Rongyao Fang, name: Yuqing Wang, name: Kun Wang, name: Linjiang Huang, name: Xingyu Zeng, name: Hongsheng Li, name: Xihui Liu

Visual generation models have made remarkable progress in creating realistic
images from text prompts, yet struggle with complex prompts that specify
multiple objects with precise spatial relationships and attributes. Effective
handling of such prompts requires explicit reasoning about the semantic content
and spatial layout. We present GoT-R1, a framework that applies reinforcement
learning to enhance semantic-spatial reasoning in visual generation. Building
upon the Generation Chain-of-Thought approach, GoT-R1 enables models to
autonomously discover effective reasoning strategies beyond predefined
templates through carefully designed reinforcement learning. To achieve this,
we propose a dual-stage multi-dimensional reward framework that leverages MLLMs
to evaluate both the reasoning process and final output, enabling effective
supervision across the entire generation pipeline. The reward system assesses
semantic alignment, spatial accuracy, and visual quality in a unified approach.
Experimental results demonstrate significant improvements on T2I-CompBench
benchmark, particularly in compositional tasks involving precise spatial
relationships and attribute binding. GoT-R1 advances the state-of-the-art in
image generation by successfully transferring sophisticated reasoning
capabilities to the visual generation domain. To facilitate future research, we
make our code and pretrained models publicly available at
https://github.com/gogoduan/GoT-R1.