Fine-tuning Flow Matching Generative Models with Intermediate Feedback

• URL: http://arxiv.org/abs/2510.18072v1
• Date: Mon, 20 Oct 2025 20:08:03 GMT
• Title: Fine-tuning Flow Matching Generative Models with Intermediate Feedback
• Abstract summary: Flow-based generative models have shown remarkable success in text-to-image generation.<n>Existing approaches solely learn from outcome rewards, struggling with the credit assignment problem.<n>We present AC-Flow, a robust actor-critic framework that addresses these challenges through three key innovations.
• Score: 23.447961856739422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow-based generative models have shown remarkable success in text-to-image generation, yet fine-tuning them with intermediate feedback remains challenging, especially for continuous-time flow matching models. Most existing approaches solely learn from outcome rewards, struggling with the credit assignment problem. Alternative methods that attempt to learn a critic via direct regression on cumulative rewards often face training instabilities and model collapse in online settings. We present AC-Flow, a robust actor-critic framework that addresses these challenges through three key innovations: (1) reward shaping that provides well-normalized learning signals to enable stable intermediate value learning and gradient control, (2) a novel dual-stability mechanism that combines advantage clipping to prevent destructive policy updates with a warm-up phase that allows the critic to mature before influencing the actor, and (3) a scalable generalized critic weighting scheme that extends traditional reward-weighted methods while preserving model diversity through Wasserstein regularization. Through extensive experiments on Stable Diffusion 3, we demonstrate that AC-Flow achieves state-of-the-art performance in text-to-image alignment tasks and generalization to unseen human preference models. Our results demonstrate that even with a computationally efficient critic model, we can robustly finetune flow models without compromising generative quality, diversity, or stability.

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