Draft-and-Target Sampling for Video Generation Policy

• URL: http://arxiv.org/abs/2603.13438v1
• Date: Fri, 13 Mar 2026 09:54:18 GMT
• Title: Draft-and-Target Sampling for Video Generation Policy
• Abstract summary: We propose Draft-and-Target Sampling, a novel diffusion inference paradigm for video generation policy.<n>Our method can achieve up to 2.1x speedup and improve the efficiency of current state-of-the-art methods with minimal compromise to the success rate.
• Score: 18.94652614447133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video generation models have been used as a robot policy to predict the future states of executing a task conditioned on task description and observation. Previous works ignore their high computational cost and long inference time. To address this challenge, we propose Draft-and-Target Sampling, a novel diffusion inference paradigm for video generation policy that is training-free and can improve inference efficiency. We introduce a self-play denoising approach by utilizing two complementary denoising trajectories in a single model, draft sampling takes large steps to generate a global trajectory in a fast manner and target sampling takes small steps to verify it. To further speedup generation, we introduce token chunking and progressive acceptance strategy to reduce redundant computation. Experiments on three benchmarks show that our method can achieve up to 2.1x speedup and improve the efficiency of current state-of-the-art methods with minimal compromise to the success rate. Our code is available.

Related papers

• TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation [61.35569005726248]
  Existing embodied control research demonstrates remarkable performance improvements by scaling training data and model size.<n>Non-deterministic generative models, such as diffusion and autoregressive models, have been widely adopted in the field of embodied control.<n>We propose textbfTapSampling, a plug-and-play framework for inference-time sampling.
  arXiv  Detail & Related papers  (2026-05-25T08:03:31Z)
• LatSearch: Latent Reward-Guided Search for Faster Inference-Time Scaling in Video Diffusion [87.42285185305813]
  We introduce a novel inference-time search mechanism that performs Reward-Guided Resampling and Pruning.<n>LatSearch consistently improves video generation across multiple evaluation dimensions compared to the baseline Wan2.1 model.
  arXiv  Detail & Related papers  (2026-03-15T18:07:29Z)
• FlowConsist: Make Your Flow Consistent with Real Trajectory [99.22869983378062]
  We argue that current fast-flow training paradigms suffer from two fundamental issues.<n> conditional velocities constructed from randomly paired noise-data samples introduce systematic trajectory drift.<n>We propose FlowConsist, a training framework designed to enforce trajectory consistency in fast flows.
  arXiv  Detail & Related papers  (2026-02-06T03:24:23Z)
• SwiftVideo: A Unified Framework for Few-Step Video Generation through Trajectory-Distribution Alignment [76.60024640625478]
  Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps.<n>We propose a unified and stable distillation framework that combines the advantages of trajectory-preserving and distribution-matching strategies.<n>Our method maintains high-quality video generation while substantially reducing the number of inference steps.
  arXiv  Detail & Related papers  (2025-08-08T07:26:34Z)
• Real-Time Iteration Scheme for Diffusion Policy [23.124189676943757]
  We introduce a novel approach inspired by the Real-Time Iteration (RTI) Scheme to accelerate inference.<n>We propose a scaling-based method to effectively handle discrete actions, such as grasping, in robotic manipulation.<n>The proposed scheme significantly reduces runtime computational costs without the need for distillation or policy redesign.
  arXiv  Detail & Related papers  (2025-08-07T13:49:00Z)
• Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification [48.17448109580635]
  We present a training-free alignment-augmented speculative decoding algorithm.<n>Our method achieves a mean acceptance length up to 2.39 and speed up generation by 2.23.
  arXiv  Detail & Related papers  (2025-05-19T14:55:41Z)
• Quantizing Diffusion Models from a Sampling-Aware Perspective [43.95032520555463]
  We propose a sampling-aware quantization strategy, wherein a Mixed-Order Trajectory Alignment technique is devised.<n>Experiments on sparse-step fast sampling across multiple datasets demonstrate that our approach preserves the rapid convergence characteristics of high-speed samplers.
  arXiv  Detail & Related papers  (2025-05-04T20:50:44Z)
• Efficient Continuous Video Flow Model for Video Prediction [43.16308241800144]
  Multi-step prediction models, such as diffusion and rectified flow models, exhibit higher latency in sampling new frames compared to single-step methods.<n>We propose a novel approach to modeling the multi-step process, aimed at alleviating latency constraints and facilitating the adaptation of such processes for video prediction tasks.
  arXiv  Detail & Related papers  (2024-12-07T12:11:25Z)
• AdaDiff: Adaptive Step Selection for Fast Diffusion Models [82.78899138400435]
  We introduce AdaDiff, a lightweight framework designed to learn instance-specific step usage policies.<n>AdaDiff is optimized using a policy method to maximize a carefully designed reward function.<n>We conduct experiments on three image generation and two video generation benchmarks and demonstrate that our approach achieves similar visual quality compared to the baseline.
  arXiv  Detail & Related papers  (2023-11-24T11:20:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.