Improving Chain-of-Thought Efficiency for Autoregressive Image Generation

• URL: http://arxiv.org/abs/2510.05593v1
• Date: Tue, 07 Oct 2025 05:40:43 GMT
• Title: Improving Chain-of-Thought Efficiency for Autoregressive Image Generation
• Authors: Zeqi Gu, Markos Georgopoulos, Xiaoliang Dai, Marjan Ghazvininejad, Chu Wang, Felix Juefei-Xu, Kunpeng Li, Yujun Shi, Zecheng He, Zijian He, Jiawei Zhou, Abe Davis, Jialiang Wang,
• Abstract summary: We introduce ShortCoTI, a lightweight optimization framework for image generation.<n>ShortCoTI rewards more concise prompts with an adaptive function that scales according to an estimated difficulty for each task.<n>Our method eliminates verbose explanations and repetitive refinements, producing reasoning prompts that are both concise and semantically rich.
• Score: 55.57836819892392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autoregressive multimodal large language models have recently gained popularity for image generation, driven by advances in foundation models. To enhance alignment and detail, newer approaches employ chain-of-thought (CoT) reasoning, expanding user inputs into elaborated prompts prior to image synthesis. However, this strategy can introduce unnecessary redundancy -- a phenomenon we call visual overthinking -- which increases computational costs and can introduce details that contradict the original prompt. In this work, we explore how to generate more concise CoT sequences for more efficient image generation. We introduce ShortCoTI, a lightweight optimization framework that encourages more concise CoT while preserving output image quality. ShortCoTI rewards more concise prompts with an adaptive function that scales according to an estimated difficulty for each task. Incorporating this reward into a reinforcement learning paradigm reduces prompt reasoning length by 54% while maintaining or slightly improving quality metrics across multiple benchmarks (T2I-CompBench, GenEval). Qualitative analysis shows that our method eliminates verbose explanations and repetitive refinements, producing reasoning prompts that are both concise and semantically rich. As a result, ShortCoTI improves computational efficiency without compromising the fidelity or visual appeal of generated images.

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