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TS-DP framework accelerates Diffusion Policy models with speculative decoding

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TS-DP, a novel framework for accelerating Diffusion Policy (DP) models used in embodied control. Unlike previous methods focused on computation, TS-DP addresses DP's I/O bound nature by employing speculative decoding. The system uses a Transformer-based drafter to replace costly denoising calls and an RL-based scheduler to adapt to varying task difficulties, achieving significant speedups and maintaining performance in simulations and real-world experiments. AI

IMPACT Accelerates inference for embodied control tasks, enabling real-time robotic applications.

RANK_REASON The cluster contains a research paper detailing a new method for accelerating AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

TS-DP framework accelerates Diffusion Policy models with speculative decoding

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The cluster contains a research paper detailing a new method for accelerating AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · 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 ·

    TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration

    arXiv:2512.15773v2 Announce Type: replace-cross 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 inferen…

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