From Broad Exploration to Stable Synthesis: Entropy-Guided Optimization for Autoregressive Image Generation
Combining Chain-of-Thought (CoT) with Reinforcement Learning (RL) improves text-to-image (T2I) generation, yet the underlying interaction between CoT's exploration and RL's optimization remains unclear. We present a systematic entropy-based analysis that yields three key insights: (1) CoT expands the generative exploration space, while RL contracts it toward high-reward regions; (2) final reward is strongly negatively correlated with both the mean and variance of image-token entropy, highlighting the need to reduce uncertainty and instability; and (3) the entropy of the textual CoT directly governs downstream image quality, with lower-entropy CoTs leading to better generations. Motivated ...
| Authors | Han Song, Yucheng Zhou, Jianbing Shen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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