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[2509.25774] PCPO: Proportionate Credit Policy Optimization for Aligning Image Generation Models
Summary
The paper introduces Proportionate Credit Policy Optimization (PCPO), a novel framework aimed at improving the stability and quality of training in text-to-image models by addressing disproportionate credit assignment issues.
Why It Matters
As image generation models become increasingly prevalent, ensuring their reliability and quality is critical. The PCPO framework addresses significant challenges in training stability and image quality, making it a valuable contribution to the field of generative AI and machine learning.
Key Takeaways
- PCPO stabilizes training processes for text-to-image models.
- The framework mitigates model collapse, enhancing image quality.
- PCPO shows superior performance compared to existing policy gradient methods.
- The approach involves a principled reweighting of training timesteps.
- Code for PCPO is publicly available, promoting further research.
Computer Science > Computer Vision and Pattern Recognition arXiv:2509.25774 (cs) [Submitted on 30 Sep 2025 (v1), last revised 24 Feb 2026 (this version, v3)] Title:PCPO: Proportionate Credit Policy Optimization for Aligning Image Generation Models Authors:Jeongjae Lee, Jong Chul Ye View a PDF of the paper titled PCPO: Proportionate Credit Policy Optimization for Aligning Image Generation Models, by Jeongjae Lee and 1 other authors View PDF HTML (experimental) Abstract:While reinforcement learning has advanced the alignment of text-to-image (T2I) models, state-of-the-art policy gradient methods are still hampered by training instability and high variance, hindering convergence speed and compromising image quality. Our analysis identifies a key cause of this instability: disproportionate credit assignment, in which the mathematical structure of the generative sampler produces volatile and non-proportional feedback across timesteps. To address this, we introduce Proportionate Credit Policy Optimization (PCPO), a framework that enforces proportional credit assignment through a stable objective reformulation and a principled reweighting of timesteps. This correction stabilizes the training process, leading to significantly accelerated convergence and superior image quality. The improvement in quality is a direct result of mitigating model collapse, a common failure mode in recursive training. PCPO substantially outperforms existing policy gradient baselines on all fronts, inclu...
