LARBRID：基于潜在噪声表征桥接的高效扩散策略微调方法
Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
AI 导读
论文提出 Latent Noise Linear MDP（LNL-MDP）及 Bridge Equation，通过对齐噪声空间与动作空间的共享表征，解决扩散与流策略 RL 微调中的“噪声混叠”问题。基于此提出算法 LARBRID，优化初始噪声分布以引导预训练模型最优行动，属于 FineTuneRL 理论框架并具近优保证。在机器人运动与操作任务上性能提升最高达 2-3 倍，代码已开源。
正文 · 原文
Abstract:Pre-trained diffusion and flow policies have emerged as powerful backbones for robotics, yet adapting them to novel data via reinforcement learning (RL) remains computationally expensive, sample inefficient and lacks theoretical guarantees. Prior approaches that fine-tune the initial noise bypass costly backpropagation through time but struggle with the "noise aliasing" problem: there does not exista natural link between the noise space value and the action space value. This paper resolves these challenges by introducing the Latent Noise Linear Markov Decision Process (LNL-MDP). We derive the Bridge Equation for LNL-MDPs which eliminates the noise aliasing problem by aligning values with a shared representation between the noise and the action space. Furthermore, our LNL-MDP fine tuning framework falls under the FineTuneRL theory with near-optimal guarantee. Leveraging these insights, we propose Latent Representation Bridging for Diffusion (LARBRID), an algorithm that efficiently learns the representation to optimize the initial distribution that steers the pre-trained model to act optimally in the environment. It yields up to 2-3$\times$ performance increases on challenging control problems across a diverse suite of robotic locomotion and manipulation tasks. Code can be found at: this http URL.
| Comments: | Conference on Neural Information Processing Systems (NeurIPS), 2026 |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.07275 [cs.RO] |
| (or arXiv:2611.07275v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07275 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuanchu Liang [view email]
[v1]
Thu, 5 Nov 2026 00:49:53 UTC (4,322 KB)
来源：arXiv cs.AI · arxiv.org