RADS：用可达性约束强化学习引导文生图扩散模型远离记忆化
Steering Away from Memorization: Reachability-Constrained Reinforcement Learning for Text-to-Image Diffusion
AI 导读
论文提出 RADS（Reachability-Aware Diffusion Steering），一种离线训练、采样时应用的调控框架，缓解文生图扩散模型的记忆化问题，同时保持生成保真度。
正文 · 原文
Abstract:Text-to-image diffusion models are susceptible to memorization, revealing a fundamental failure to generalize beyond the training set. Current mitigation approaches often sacrifice image quality or prompt alignment to reduce memorization. To address this, we propose Reachability-Aware Diffusion Steering (RADS), an offline-trained, sampling-time control framework that mitigates memorization while maintaining generation fidelity. RADS models the diffusion denoising process as a dynamical system and learns a policy-conditioned reachability value function that estimates future memorization risk along the denoising trajectory. This motivates a constrained reinforcement learning (RL) formulation, where a policy learns to steer the trajectory away from memorization via perturbations in the caption embedding space. Empirical evaluations show that RADS achieves a stronger empirical Pareto frontier between generation diversity (SSCD), quality (FID), and alignment (CLIP) compared to state-of-the-art inference-time baselines. Crucially, RADS applies the learned controller during sampling time, separating memorization mitigation from the pretrained model. Our website is available at: this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.00140 [cs.CV] |
| (or arXiv:2603.00140v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.00140 arXiv-issued DOI via DataCite |
Submission history
From: Sathwik Karnik [view email]
[v1]
Tue, 24 Feb 2026 09:07:08 UTC (16,350 KB)
[v2]
Wed, 11 Nov 2026 23:25:27 UTC (43,202 KB)
来源：arXiv cs.AI · arxiv.org