Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance

• URL: http://arxiv.org/abs/2508.21016v1
• Date: Thu, 28 Aug 2025 17:18:31 GMT
• Title: Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance
• Authors: Luozhijie Jin, Zijie Qiu, Jie Liu, Zijie Diao, Lifeng Qiao, Ning Ding, Alex Lamb, Xipeng Qiu,
• Abstract summary: We introduce Reinforcement Learning Guidance (RLG), an inference-time method that adapts Dejin-Free Guidance (CFG)<n>RLG consistently improves the performance of RL fine-tuned models across various, RL algorithms, and downstream tasks, including human preferences, compositional control, compress, and text rendering.<n>Our approach provides a practical and theoretically sound solution for enhancing and controlling diffusion model alignment inference.
• Score: 46.06527859746679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denoising-based generative models, particularly diffusion and flow matching algorithms, have achieved remarkable success. However, aligning their output distributions with complex downstream objectives, such as human preferences, compositional accuracy, or data compressibility, remains challenging. While reinforcement learning (RL) fine-tuning methods, inspired by advances in RL from human feedback (RLHF) for large language models, have been adapted to these generative frameworks, current RL approaches are suboptimal for diffusion models and offer limited flexibility in controlling alignment strength after fine-tuning. In this work, we reinterpret RL fine-tuning for diffusion models through the lens of stochastic differential equations and implicit reward conditioning. We introduce Reinforcement Learning Guidance (RLG), an inference-time method that adapts Classifier-Free Guidance (CFG) by combining the outputs of the base and RL fine-tuned models via a geometric average. Our theoretical analysis shows that RLG's guidance scale is mathematically equivalent to adjusting the KL-regularization coefficient in standard RL objectives, enabling dynamic control over the alignment-quality trade-off without further training. Extensive experiments demonstrate that RLG consistently improves the performance of RL fine-tuned models across various architectures, RL algorithms, and downstream tasks, including human preferences, compositional control, compressibility, and text rendering. Furthermore, RLG supports both interpolation and extrapolation, thereby offering unprecedented flexibility in controlling generative alignment. Our approach provides a practical and theoretically sound solution for enhancing and controlling diffusion model alignment at inference. The source code for RLG is publicly available at the Github: https://github.com/jinluo12345/Reinforcement-learning-guidance.

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