Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance
  for Text-to-Image Generation
Authors: name: Yaqi Li, name: Peng Chen, name: Mingyang Han, name: Bu Pi, name: Haoxiang Shi, name: Runzhou Zhao, name: Yang Yao, name: Xuan Zhang, name: Jun Song

Despite the promising progress of recent autoregressive models in
text-to-image (T2I) generation, their ability to handle multi-attribute and
ambiguous prompts remains limited. To address these limitations, existing works
have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and
employed reinforcement learning (RL) to improve reasoning capabilities.
However, most models provide reward signals only at the end of the generation
stage. This monolithic final-only guidance makes it difficult to identify which
stages contribute positively to the final outcome and may lead to suboptimal
policies. To tackle this issue, we propose a Visual-Chain of Guidance
(Visual-CoG) paradigm consisting of three stages: semantic reasoning, process
refining, and outcome evaluation, with stage-aware rewards providing immediate
guidance throughout the image generation pipeline. We further construct a
visual cognition benchmark, VisCog-Bench, which comprises four subtasks to
evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on
GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%,
5%, and 19%, respectively, demonstrating the superior performance of the
proposed Visual-CoG. We will release all the resources soon.