# Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising

arXiv: https://arxiv.org/abs/2510.21991 · HTML: https://mlsanity.com/paper/2510.21991/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models.
- Builds on 22 of its 38 references that are in the map.
- First posted on arXiv 24 Oct 2025.

## Placed in

- Diffusion models / [Diffusion and flow policies for robotics](https://mlsanity.com/diffusion/diffusion-and-flow-policies-for-robotics/)

## Builds on (most cited first, 22 of 22)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [D4RL: Datasets for Deep Data-Driven Reinforcement Learning](https://mlsanity.com/paper/2004.07219/) (2020)
- [Planning with Diffusion for Flexible Behavior Synthesis](https://mlsanity.com/paper/2205.09991/) (2022)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [One Step Diffusion via Shortcut Models](https://mlsanity.com/paper/2410.12557/) (2024)
- [The Intrinsic Dimension of Images and Its Impact on Learning](https://mlsanity.com/paper/2104.08894/) (2021)
- [Align Your Steps: Optimizing Sampling Schedules in Diffusion Models](https://mlsanity.com/paper/2404.14507/) (2024)
- [Fast ODE-based Sampling for Diffusion Models in Around 5 Steps](https://mlsanity.com/paper/2312.00094/) (2023)
- [A Comprehensive Survey on Knowledge Distillation of Diffusion Models](https://mlsanity.com/paper/2304.04262/) (2023)
- [Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity](https://mlsanity.com/paper/2405.15986/) (2024)
- [Your diffusion model secretly knows the dimension of the data manifold](https://mlsanity.com/paper/2212.12611/) (2022)
- [InferGrad: Improving Diffusion Models for Vocoder by Considering Inference in Training](https://mlsanity.com/paper/2202.03751/) (2022)
- [Generative Models in Decision Making: A Survey](https://mlsanity.com/paper/2502.17100/) (2025)
- [SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation](https://mlsanity.com/paper/2403.01505/) (2024)
- [On the Global Convergence of Particle Swarm Optimization Methods](https://mlsanity.com/paper/2201.12460/) (2022)
- [Density control of large-scale particles swarm through PDE-constrained optimization](https://mlsanity.com/paper/2104.06373/) (2021)

## Built on by (most cited first, 8 of 8)

- [Drift-Based Policy Optimization: Native One-Step Policy Learning for Online Robot Control](https://mlsanity.com/paper/2604.03540/) (2026)
- [CoFL: Continuous Flow Fields for Language-Conditioned Navigation](https://mlsanity.com/paper/2603.02854/) (2026)
- [Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models](https://mlsanity.com/paper/2606.14375/) (2026)
- [The Speedup Paradox: Rethinking Inference Speed-Quality Trade-off in Embodied Tasks](https://mlsanity.com/paper/2606.28529/) (2026)
- [VADF: Vision-Adaptive Diffusion Policy Framework for Efficient Robotic Manipulation](https://mlsanity.com/paper/2604.15938/) (2026)
- [NavCMPO: Critic-Guided MeanFlow Policy Optimization for Adaptive Navigation](https://mlsanity.com/paper/2607.14643/) (2026)
- [A Hierarchical Approach to Imitation Learning for Manipulation Tasks Requiring Time Varying Forces](https://mlsanity.com/paper/2608.03103/) (2026)
- [Optimizing H-Graph Hybridization for Diffusion-Guided RRT](https://mlsanity.com/paper/2609.32897/) (2026)

## Abstract (arXiv)

Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originally developed for vision tasks like image and video generation, many of their inference strategies have been directly transferred to control domains without adaptation. In this work, we show that by tailoring the denoising process to the specific characteristics of embodied AI tasks -- particularly structured, low-dimensional nature of action distributions -- diffusion policies can operate effectively with as few as 5 neural function evaluations (NFE). Building on this insight, we propose a population-based sampling strategy, genetic denoising, which enhances both performance and stability by selecting denoising trajectories with low out-of-distribution risk. Our method solves challenging tasks with only 2 NFE while improving or matching performance. We evaluate our approach across 14 robotic manipulation tasks from D4RL and Robomimic, spanning multiple action horizons and inference budgets. In over 2 million evaluations, our method consistently outperforms standard diffusion-based policies, achieving up to 20% performance gains with significantly fewer inference steps.