GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation
  with Reinforcement Learning
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.
682ffa9a6e906040a3bb7160
Chengqi Duan
claimed_verified
2025-09-17T13:14:21.010Z
64a2b496e2e19de17db7de65
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Duan Chengqi
user
gogoduan
682ffa9a6e906040a3bb7161
Rongyao Fang
claimed_verified
2025-05-26T08:15:13.374Z
65b8724123d948d884b379b1
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Rongyao Fang
user
LucasFang
682ffa9a6e906040a3bb7162
Yuqing Wang
claimed_verified
2025-06-25T09:19:37.039Z
63ea23b9dedfeebe54d02bdf
/avatars/4d9f9a546aa8c63e277161ea700075c4.svg
Yuqing Wang
user
Epiphqny
682ffa9a6e906040a3bb7163
Kun Wang
682ffa9a6e906040a3bb7164
Linjiang Huang
682ffa9a6e906040a3bb7165
Xingyu Zeng
682ffa9a6e906040a3bb7166
Hongsheng Li
682ffa9a6e906040a3bb7167
Xihui Liu
claimed_verified
2025-05-26T08:15:11.367Z
65d5ec74cd05bc1eaa125040
/avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
Xihui Liu
user
XihuiLiu
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation
  with Reinforcement Learning
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.
GoT-R1 enhances visual generation by using reinforcement learning to improve semantic-spatial reasoning, outperforming existing models on complex compositional tasks.
reinforcement learning Generation Chain-of-Thought MLLMs dual-stage multi-dimensional reward framework semantic alignment spatial accuracy visual quality T2I-CompBench