arXiv cs.AI· Ye Li, Jiahe Feng, Yuan Meng, Kangye Ji, Chen Tang, Jiajun Fan, Xinwan Wen, Jiawei Li, Wenwu Zhu, Shu-Tao Xia, Xin Wang, Zhi Wang·· 3 小时前AI 评分47
TS-DP：面向时间自适应 Diffusion Policy 加速的强化推测解码框架
TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration
AI 导读
研究者提出 TS-DP，首次将推测解码用于加速 Diffusion Policy，并针对任务难度随时间变化的具身场景设计蒸馏 drafter 与 RL 调度器。仿真中实现最高 4.17x NFE 降低和 3.2x 实际加速，草稿接受率超 85%。真实世界实验达到 3.8x NFE 降低、26 Hz 推理频率，传送带任务成功率提升 23%，代码已开源。
正文 · 原文
Authors:Ye Li, Jiahe Feng, Yuan Meng, Kangye Ji, Chen Tang, Jiajun Fan, Xinwan Wen, Jiawei Li, Wenwu Zhu, Shu-Tao Xia, Xin Wang, Zhi Wang
Abstract:Diffusion Policy (DP) excels in embodied control but suffers from high inference latency and computational cost due to multiple iterative denoising steps. Existing diffusion model (DMs) acceleration methods improve inference speed by reducing computational overhead, as DMs for image generation are computation-bound. In this work, we reveal that DP are I/O bound, and thus propose using speculative decoding, a parallel computing framework, to accelerate DP for the first time. However, it is non-trivial to address the following challenges: (1) How to preserve the denoising quality of the base model at reduced computational cost under time-varying task difficulty in embodied settings; and (2) How to dynamically allocate computation according to task difficulty in such environments. Therefore, we propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP), the first framework that enables speculative decoding for DP with temporal adaptivity. First, to handle dynamic environments where task difficulty varies over time, we distill a Transformer-based drafter to imitate the base model and replace its costly denoising calls. Second, an RL-based scheduler further adapts to the time-varying task difficulty by adjusting speculative parameters to maintain accuracy while improving efficiency. Extensive experiments show that TS-DP achieves up to a 4.17x NFE reduction and a 3.2x wall-clock speedup with a draft acceptance rate of over 85% in simulation. Real-world experiments further demonstrate a 3.8x NFE reduction while improving success rates on real-time tasks (e.g., a 23% gain on the conveyor-belt task), reaching an inference frequency of 26 Hz and enabling real-time control while maintaining comparable task performance. The code is available at this https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2512.15773 [cs.LG] |
| (or arXiv:2512.15773v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2512.15773 arXiv-issued DOI via DataCite |
Submission history
From: Ye Li [view email]
[v1]
Sat, 13 Dec 2025 07:53:14 UTC (8,742 KB)
[v2]
Tue, 27 Oct 2026 02:37:43 UTC (28,808 KB)
来源：arXiv cs.AI · arxiv.org