In this episode, we discuss Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step by Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Peng Gao, Hongsheng Li, Pheng-Ann Heng. The paper investigates the use of Chain-of-Thought (CoT) reasoning to improve autoregressive image generation through techniques like test-time computation scaling, Direct Preference Optimization (DPO), and their integration. The authors introduce the Potential Assessment Reward Model (PARM) and an enhanced version, PARM++, which evaluate and refine image generation for better performance, showing significant improvements over baseline models in benchmarks. The study offers insights into applying CoT reasoning to image generation, achieving notable advancements and releasing code and models for further research.
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