LARBRID：通过潜在噪声表示桥接实现扩散策略高效 RL 微调
Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
机器人扩散策略想用 RL 微调但算不动？这篇 LARBRID 只优化初始噪声分布，速度提上去了，成绩还能涨 2-3 倍，代码开源可以跑。
arXiv 论文提出 LNL-MDP（Latent Noise Linear Markov Decision Process）框架，针对扩散与流策略微调中的“噪声混叠”问题，推导出 Bridge Equation，使噪声空间与动作空间的值函数对齐到共享表示。基于此提出 LARBRID 算法，通过优化初始噪声分布来引导预训练模型，且在 FineTuneRL 理论下具有近优保证。在机器人运动与操作任务上性能提升最高 2-3 倍，代码已在 GitHub 开源。
Efficient Diffusion Policy Fine Tuning with Latent Noise Representation Bridging
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: github.com/Maincode-Research/LARBRID.git.