Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Authors: Mateo Clémente
Leo Maxime Brunswic
Rui Heng Yang
Xuan Zhao
Yasser H. Khalil
Haoyu LEI
Amir Rasouli
Yinchuan Li
Venue: NeurIPS 2025 poster ()
Keywords: diffusion models
shortcut models
fast diffusion
clipping
DDPM
diffusion policy
robotic manipulation
embodied AI
Unet
metaheuristic
genetic algorithm
D4RL
robomimic
Primary area: deep_learning
TL;DR: We show that diffusion policy  can be made faster and more accurate without retraining and analyse why, we introduce a genetic algorithm to reduce to two denoising steps..

Abstract: 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 the 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.

Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Mateo Clemente* Huawei Technologies Canada mateo. clementeeh www.eeetttoo-aarrrrssoooeo partners.com
Léo Maxime Brunswic*
Huawei Technologies Canada 1eo maxime. brunswicch- www.wiichhaattesscoomm com
Rui Heng Yang
Xuan Zhao
Huawei Technologies Canada rui heng. yang@huawei..cmmmm com
Huawei Technologies Canada
xuan zhaoCh wwtttarratttttteeeeeeeeeee com
Yasser H. Khalil
Haoyu Lei
Amir Rasouli
Huawei Technologies Canada yasser khali il @huawei. com
Huawei
Huawei Technologies Canada
lei www.hhuuuaeie.ccom
amir. rasoul @huavei com
Yinchuan Li" Huawei 1 Ayyinchuun@haavei.coo
Abstract
Diffusion models, such as diffusion policy, have achieved state-of-the-art results 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 control domains without adaptation. In this work, we show that by tailoring the denoising process the specific characteristics of embodied AI farks-particularly the structured, lww-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-distributio 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. over 2 million evaluations, our method consistently outperforms standard diffusion-based policies, achieving up to 20% performance gains with significantly fewer inference steps.
Introduction
Stochastic policies have become increasingly important in robotic manipulation and more generally Embodied Artificial Intelligence (EAI), where agents must operate in real-world environments typically involving large action spaces, possibly stochastic responses, and multiple valid strategies for achieving the same objective [I]. These challenges are further compounded by data scarcity and
Equual contributions
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

the need for strong generalization from limited demonstrations, Diffusion basedd policies 2 3 offer a promising solution by learning model the full distribution over expert actions, thereby mitigating mode collapse and enabling diverse robust behavior.
Despite their success, diffusion mod- els suffer from a key drawback: in- ference sequential and computation- ally expensive, requiring many denois- ing steps produce high-quality sam- ples. This latency is major limitation for real-time applications in roboties, where fast and reliable action genera- tion is critical. address this, recent works have proposed distillation [4]. consistency models [). and short- cut flow-matching 7.. which trade off
100 stap DONA
2-step Shortaatt
2-step GDP (oun))
1.0
0.5
0.0
Pen
Door
Relocate
Hammer
Figure I: Comparison of normalized scores of Genetic Dif- fusion Policy to shorteut and diffusion policy baselines on Adroit Hand tasks.
simplicity or performance for faster sampling by training new models.
In this work, we accelerate off-the-shelf diffusion policies without any retraining or architecture changes. We show that by reducing the number of inference steps and modifying the denoising schedule, we can often improve performance. Our analysis reveals that the default inference process suffers from nut-of-distribute to constrain predictions. Contrary to findings in image generation, we observe that reducing the injected noise in denoising steps improves performance in robotic tasks, due to the structured and low-dimensional nature of their action distributions. These findings emphasize a critical distinction: techniques that enhance image generation models do not necessarily transfer to embodied AL Rather than blindly adopting heuristics from vision, robotic policy models require dedicated analysis of their training dynamics, action spaces, and inference behavior.
(OoD) intermediate states caused by clipping- heuristic introduced
To this end, we introduce the Genetic Diffusion Policy (GDP), see Figure 2 which uses a population- based selection mechanism to filter denoising trajectories based on an OoD score, reducing clipping artifacts and improving sample quaecal-sspeciallyy at step counts.
Compute Scores
Multinomial Selection
Denoise
Noisy samples
Figure 2: Genetic Denoising Process. One starts from pure Gaussian noisy samples, fitness scores are computed and used weights for multinomial selection. Selected samples duplicated to replace deleted samples. Then, apply a denoising step following possibly twisted DDPM denoising step. not terminal denoising step loop back to computing fitness scores and repeat. By choosing fitness score measuring whether samples are in distribution, the method favor more denoising trajectories with more precise denoising estimations hence more precise sampled actions.
We evaluate our method on 14 manipulation tasks from D4RI. g and Robomimic 9 covering 6 action horizons and 18 inference budgets, using up to 500 seeds per configuration. Baselines include
DDPM/DDIM [10 11, EDM [2], and shortcut diffusion models 7 Figure summarizes our results on Adroit Hand tasks. In summary, our main contributions are:
Efficient Denoising: We propose simple yet effective modifications to the denoising schedule that enable faster, more accurate sampling from existing diffusion policies.
Theoretical and Empirical Analysis: We identify and explain the role of OoD noise and the counterintuitive benefits of reduced noise injection lov-dimensional robotic settings.
Genetic Diffusion Policy (GDP): We introduce a novel sampling strategy that uses pop- ulation of candidate denoising trajectories and selects in-distribution paths to improve

---

robustness and performance at low inference budgets. To our knowledge, our method is the first attempt employing genetic algorithms to accelerate diffusion model sampling.
Making better use of Diffusion Policies
2.1 Diffusion Policies
Robotic manipulation tasks are naturally modeled as Markov Decision Processes (MDPs), i.e. tuples (S, O,A, T, R, #). Here S denotes the of environment states, O the of observations, A the action space, T": S x S the (possibly stochastic) transition kernel, and R: S + Reo a terminal- reward function. At time i the physical world-including the robot-is in some 8, ES, while the agent only perceives a partial observation o, E and selects an action 0r 6 according to a policy T: after which the environment transitions 8 T(s,,a)).
Training. Diffusion policies
2 adapt the diffusion-model paradigm 10 to decision making.
Given demonstration data set D {(o)) (i) ) comprising successful episodes, construct a distribution
1a O aceths)
Ohe x A
ie. windows length ho past observations and ha future actions centered at some т. For numerical stability, we assume A embedded in Rd and rescaled to [-1,1]².
For chosen noise schedule (a:)Eo. the network eq : AA x {0, "O,,"TTT.. x]x + Ah is trained by minimizing the Denoising Diffusion Probabilistic Model (DDPM) loss
(())=
(o.z))-u co(Va Vi (-.T).T)
Vi-a 1,0) < with 04
O
(1)
t-Unif[T]
Inference.
At test time we denoise an initial 2r ~ (0,I) using
(1) Tt V1 a2 Eg (xt)
ft-1 = 02-1 clip
Tr - of (9 (xx) 40
Vag
where of := 7 (1=ا 1) 1_.))
Tt ((1-a))) 17E [0, 1] interpolates between the deterministic DDIM sampler
(17 = 0) and the stochastic DDPM sampler (17 1), and y= in the standard formulation. Equation (2)
is a finite-difference discretizations of the Stochastic Differential Equation (SDE) derived in [12)
dx. (1) (1) V=logp(>,, (, di -B(())²()) Valogp(> 1, di r3/23(( (1) dW, (3)
coupling the probability-fi ODE with a Langevin diffusion. Separating the training horizon T
from a possibly reduced number of inference steps 6 is straightforward: replace (t,t-1) in 22 and in
σe by (t,, for a monotone schedule S to < ts S.
2.2 Hlipping-Induces Denoising Defects
The clip operation in (2) serves two independent purposes. First, robotic actions are normalized to [-1,1] so any estimator &o lying outside that cube must be projected back. Second, for the first few timesteps of cosine noise schedule [13 have a₁ & O, making the denominator Voe ill-conditioned; clipping prevents numerical explosions.
Unfortunately, this crude safeguard creates subtle distributional mismatch. Near t t= T most coordinates of it saturate at 1),, so the inference distribution x, ~ (N(0, @ (fi o) is supported almost exclusively the corners of the hyper-cube, whereas training sees sees (0, (u) o), whose conditional is spread throughout the interior. Consequently,
1. early denoising steps convey little task-relevant signal;
2. the predictor eg incurs larger errors (because it never learned to handle these extreme inputs);
3. the overall reverse process wastes iterations before trajectories re-enter the high-density region of the noised target action manifold.

---

Figure quantifies this phenomenon across tasks and sampler hyper--prannnn parameters: the higher the clipping frequency, the lower the final episodic return. Similar training-sampp sampling discrepancies were found to degrade quality in other domains and have motivated methods such as InferGrad [14), which explicitly harmonize the two regimes.
2.3 Exploration-EExllotttoo trade-off in Diffusion Policy for Robotics
The link between clipping frequency and score suggests that significant improvement may be achieved by training a diffusion model well so that it generates good policy, possibly with many denoising steps, then tweaking the denoising process to obtain sufficient sample quality with less denoising steps. We argue that the parameters v and y allow an exploration-exploiatiii leverage to mitigate the clipping issue. As discussed in the introduction, generalization requires to train a stochastic policy mitigate mode collapse during training. However, mode collapse is not an issue during inference: robot that always chooses the same solution given the same context is acceptable as long as successful. In other words, if the diffusion model is well trained and fits the whole training distribution, exploitation in the sense of outputting less diverse but acceptable actions should not be an issue.
trade-off that one may
On the one hand, looking at Equation 3 by reducing the noise injection scale 7, our de- noising process collapses to the deterministic probability flow (which a gradient ascent of the noise target distribution) with noise decay.
1.0
1.00
0.95
0.8
Noise in injection scale
Normalized Cormalizedaae score
0.90
0.6
The smaller the y, the more likely the gen- erated sample lies close to the maximum of density of a dominant mode in the target ac-
0.85
Task
tion distribution. We favor exploitation over exploration.
0.75
Pen
0.2
Relocate
0.70
Door
On the other hand, lack of exploration may result in missing the best modes. Reduction of noise injection and number of denoising steps results in a bias toward modes closer to O. In the context of image generation it results in caricatural outputs: as depicted in Figure 4 people portrait generation yields monstrous low contrast faces with protruding eyes. Few-steps with little noise injection is putting a strain on the ability the denoising process to find good modes.
Hammer
0.0
0.65
0.00
0.05
0.10
Clipping frequency at t=5
Figure 3: Fach marker represents unique combina- tion of task, denoising-step count, and noise-injection scale, all at denoising step t= 5. Clipping frequency is calculated the proportion of entries in the flattened, noised action-sequence tensor that are projected onto the cube boundary. Grey lines show fourth-order poly- nomial regressions fitted separately for each task.
Furthermore, robotic manipulation distribu- tions conditioned by observations are intrinsically low dimensional and simpler than image distri- butions: the image dataset CelebA has extrinsic dimension 26 intrinsic dimension around 25 (IS] while the action space of Adroit Hand environment varies from 24 to 30 with an estimated intrinsic dimension 11 at action horizon 24 for most tasks, see Appendix B Also, Robotic tasks being MDP and assuming the action horizon is small enough, mistakes may be corrected as long as they are not fatal. This translates into robustness against imperfect policies.
2.4 Simple Empirical Solutions
The discussion of the previous section suggests simple ways to improve our usage of the Exploration- Exploitation tradeoff. First, likely that a well-chosen denoising time schedule (t)))e((.....))) allows to reduce the number of needed steps. Starting the denoising process from la T eliminates an uninformative step, the normal distribution from which øt, is drawn already covers the noised target distribution. Also, less steps means less injected noise, less clipping and less OoD values hence better quality of noise prediction (0(2 f, o).
Second, reduce the noise injection scale favoring exploitation, reducing the probability of denoising into tensor of larger values hence larger clipping frequency.

---

Third, use clipping- trpping--freeee denoising process. One may replace the DDPM scheduler with DDIM scheduler. In Chi al. B DDIM was used as way to do less denoising steps transposing the initial purpose of Song al. 11. however they do not benchmark DDIM in emulated environments. Our experiments found that DDIM do not perform particularly well. This is not fortuitous: DDIM compensates the lower noise with larger deterministic steps, which are unreliable at the beginning of the denoising process due to OoD partially de- noised samples. A more principled training and sampling that denote by EDM was proposed by Karras et al. 12 principle, one could use off-the-sheif DDPM Diffusion Policy model and use EDM sampler. However, in order to obtain competitive results, not only we had retrain EDM policy from scratch but also had tune training hyperparameters (see Experiments section 4). Unlike DDPM, the EDM policies very unreliable across horizons, going from solving environement in horzion 24 to not even reaching 50% success rate as shown in figure.
Figure 4: Diffusion cetuusioonngnerateedd face without noise injection.
The first two solutions above easy to implement and yield significative improvement over baseline as the Experiments section suggests. However, further improvement seems rather limited as these methods do not give much room to improve the aforementioned trade-off.
3 Genetic Diffusion Policy
The Exploration-EExplottiiii trade-off discussed in the last section is solved in diffusion for image generation by long denoising trajectories. However, in terms of complexity constraints, EAI is polar opposite of Image generation: image generation is limited the memory complexity of algorithms because of the high dimension of the distributions, but time is not much of an issue image generation tasks rarely require very high reactivity. On the other hand, EAI requires fast generation, but is less constrained memory-wise thanks to the low dimensionality of the action space. Our solution to improve both exploration and exploitation is to leverage this specificity of EAI by enhancing the denoising process using genetic algorithm. The heuristics we choose for our genetic algorithm measures how OoD given sample is.
Algorithm Genetic Diffusion Policy
Require: Diffusion Policy noise model e with schedule (ar) (a)))e[o,7] Stochastic denoising rule
fty-; = (xi,,,,, OoD score p( ty, co +t)). Population size P. Survival number S, Denoising steps N.
1: Sample iiny ~ (O,,1) for (I,"..P)
2:
N
3: while j 0 do
4:
j j+ --j1
5: Compute scores p: := (ty,,,t,,
6: Select element in {1, "., P} with (i), (S.
xi,-1 -D iis E lins t2
7:
8: end while
9: Return x
In details, generate population of noised samples; Before applying a denoising step, we compute a fitness score for each partially denoised sample, then select half of the sample using a Multinomial sampler weighted by the fitness scores, and finally duplicate the selected samples to fill the population batch. Then a usual denoising step is applied to the population. Our fitness score chosen to enhance diffusion models by favoring in distribution samples. Two families of scores ip considered:
Pstein. ( Tx x (()co and (elp...T(z(.t)) T f CLIP(&o)),
where T is temperature factor, f is scaling function and 4. 1-- See Algorithm
The clip-based score family Pelip.f.TT is clearly motivated by the discussion of section 2.2 both thcoretically and empirically. Theoretically, clipping occurs when &o is OoD, hence the importance
5

---

Table I: Adroit Hand results. Normalized success rates averaged 100 seeds. DDIM and ablation variants are integrated. "Schedule" refers to adapted schedule; "Best 7 refers to best reduced noise scale (y=0.2).
Method
Steps
?
Hammer
Relocate
Pen
Door
Full diffusiio schedule (100 steps)
DDPM
100
0.68
0.69
0.88
0.87
DDPM
100
0
0.99
0.95
0.94
1.00
Shortcut
100
0.70
0.84
0,81
0.87
DDIM
100
0.70
0.38
0.50
0.83
GDP
100
0.2
0.99
0.98
0.94
1.00
Few-step inference (5 steps)
DDPM
0.91
0.91
0.85
1.00
DDPM
5
0
0.99
0.97
0.84
1.00
Shortcut
0.88
1.00
0.81
0.94
DDIM
5
0.71
0.38
0.70
0.81
GDP
0.2
1.00
0.99
0.91
1.00
Minimal inference (2 steps)
DDPM
0.00
0.01
0.13
0.01
DDPM
2
0
0.00
0.02
0.11
0.01
Shortcut
0.88
1.00
0.81
0.94
DDIM
2
0.76
0.42
0.73
0.95
DDPM Schedule
0.87
0.64
0.74
0.97
DDPM + Schedule
2
0
0.95
0.74
0.75
1.00
DDPM + Schedule Best yy
0.2
0.98
0.92
0.89
1.00
GDP
2
0.2
1.00
0.98
0.91
1.00
Shortcut
0.83
0.93
0.74
0.89
of clipped coordinates is measure of OoD. Empirically, if clipping is the issue, favoring less clipped samples should yield significant improvement sample quality.
The mein-based score f.t has a double theoretical motivation. First, reducing the noise injection breaks the Langevin process, stein-fitness emulates the noise injection of the Langevin process. Second, the noise estimator is direct measure of OoD since high noise means that the sample is far away from every modes of the target distribution.
4 Experiments
4.1 Experimental Setup
We evaluate hypotheses that (i) reducing the number of inference iterations and (ii) lowering the noise injection scale both mitigate clipping and improve performance. Experiments are conducted on the Adroit Hand [16), see figure I and Robomimic (9] benchmarks. Each Adroit task involves controlling a 24-30 DoF robotic hand to accomplish distinct goal:
Pen: Orient a pen to target angle.
Relocate: Grasp a ball and place it at goal position.
Hammer: Pick up hammer and strike nail.
Door: Pull down the latch and open the door.
All methods use the same UNet architecture with 65M parameters from the official Diffusion Policy (DP) implementation 17 to ensure fairness. Each Adroit configuration is evaluated on 100 seeds, and each Robomimic configuration on 500 seeds.
We sweep over the following inference hyperparameters:
Number of inference steps å 21, 10) u 30, ... 100).

---

Noise scaling factor y (0.0,0.1....... 1.0),
Action horizon ha é {24, 48, 76, 100, 152, 200).
Sampling method: DDPM, GDP, DDIM or Shortcut.
Since publicly released Adroit checkpoints do not cover all action horizons. we retrained each diffusion policy using the DP pipeline with AdamW [9] learning rate 10 weight decay 10-6, batch size 64, and 200 epochs. Shortcut models 7 were re-implemented in PyTTrch and trained via 10 random hyperparameter seeds per task-horizon pair, keeping the best model. All base models were trained for 100 diffusion steps (128 for Shortcut) and evaluated with a subsampled cosine inference schedule. linear schedule was also tested but underperformed consistently across all methods. The same DDPM-trained checkpoint is used for DDPM, GDP and DDIM, while the shortcut model is trained separately.
We then test the proposed Genetic algorithm with a very coarse parameter grid as the goal is to justify the use of the algorithm[ rather than tuning it to its most optimal state. We test populations p [4, 8, 16, 32], temperatures & [1, 10, 100, 1000]. and noise scales Y E [1, 0.6, 0.3, 0.2,
4.2 Results
Number of diffusion steps- The correlation between the number of steps n and clipping evident across all tasks: reducing the number of diffusion steps lowers the amount of clipping. This leads to similar or better scores with greatly reduced inference cost. We observe a peak, prior
4, at which the clip is minimal, i.e. the performance is optimal, see figure [(a) The location of the peak is slightly task dependent but robust to different seeds. This allows one to tune the model to the optimal number of steps, without it breaking in unfamiliar situations.
Noise injection scale The reduced noise inference processes also behave as expected: lowering the injected noise reduces clipping significantly. However, as shown in Figure the extent which the clipping can be mitigated via noise reduction is limited. This means that part of the clipping is inherent to the deterministic part of the denoising process, and not only the noise injection from the Langevin process. Still, the increase in score resulting from using lower noise scales is significant, see figure (b)) With this noise rescaling, the same UNet can go from a 75% success rate to totally solving the task. This also stabilizes the score in higher numbers of denoising step, bridging the gap between the peak and the rest of the distribution.
Horizon Throughout all our experiments, all the behaviors mentioned in the previous paragraphs are present across horizons. We notice that intermediate horizons seem to be harder than either short, or full length horizons. Our hypothesis for this phenomenon is that by increasing the horizon, tradeoff conditioning complexity for distribution complexity. In longer horizons, the model has to learn few complex distributions whereas in shorter horizons, the distributions are simpler but the conditioning needs to encapsulate more of the dynamics of the environment. This would lead to a dip where the tradeoff's is suboptimal, with significant number of non trivial distributions learn. This tendency was even more present with EDM, as showed in Figure (cC)
Shortcut Models In this experiment, we evaluate our approach against the shortcut models. Shortcut models 77. as prominent means of increasing sampling speed, train a model with conditioning on the number of steps that will taken in total, which enables outputting quality samples one step. However, this speedup comes at the price of performance degradation. As shown in Frans et al. al. DP's success rate can be reduced by as much as 20% on tasks such as the robomimic transport [ task. We train and tune shortcut models for all four Adroit tasks, conducting 10 training trials for each. The reported performance for the shortcut method on each environment is the highest score achieved across the 10 trained models.
The results in Table I demonstrate that although the shorteut method is the only one capable of achieving 1-step sampling, its performance falls significantly short. Our approach to 2-step generation combines the GDP technique with the timestep adjustments described in Section 2.4, where we set the maximum timestep to 90 and the minimum to 20. This method outperforms baselines or is at least on par regardless of the number of diffusion steps while requiring only two steps. Starting from solid off- off-the-shhe shelf model with approximately 70% success rate, method achieves 100%
7

---

Normaloon clipping (twe)
0..8
035
Normnltee Score
Strps
90
24
Task
0R5
Door
0.2
Relocate
100
152
Hammer
0.2
Stops
injection
(a): Clip frequency vs. denoising (b): Effect of noise injection across steps (Adroit).
(c): EDM: vs. horizon (few- step).
horizons.
Figure 5: (a) Clip frequency normalized by the maximum observed value as a function of the number of denoising steps; clipping increases with more steps. (b) Impact of noise injection scale across action horizons, averaged over tasks; lower injection improves performance systematically. (c) Normalized score across horizons for tuned EDM in the few-step regime (for n > 5, curves match = 5).
Hammer
Relocate
Pen
Door
1.0
0.8
0.6
DDPM
0.4
EDM
0.2
GDP
Shortcut
2 3
10 20
50 100
2.
20 Iteration stops
100
2 3.3 10 20 50 100
2 3 5 10 20
50 100
[toration stops
Iteration stops
[teration stops
Figure 6: Performance across timesteps for tasks.
success rate along with substantial speedup. Our experiments also showed that GDP suffers from using y We posit that this is due to the noise enabling individuals from the populations to "jump" from mode to mode even at later steps, causing mode collapse due to the survivor selection process.
4.3 Inference Overhead and Wall-Clock Runtime
We measure step-wise overhead on an RTX 3080, batching the population in single forward pass per step. The NFE cost is the wall-time of single call of the model on the population; the Step cost is the wall-time of the denoising step after NFE: computation of formula computation of fitness score and population management. See table
5
Related Works
First, the Shortcut baseline maybe seen self-distillated consistency model 5 4 More traditional
knowledge distillation methods maybe employed to accelerate diffusion models 20
Second, fast generation of diffusion models is very active research thread mostly trying to improve
the Stochastic Differential Equation (SDE) solver using the denoising process [2 22 while other approaches try to leverage parallel sampling [23] This last method may be compared to as
their averaging method may be interpreted as cross- brees--beeeingg step. More generally. swarm methods for solving Partial Differential Equations (PDE) is active research thread (24). Via Fokker-Plance equations, the PDE and SDE view points are dual to one another.
Third, by leveraging simple metaheuristic sample population, our Genetic Denoising Process open the door for a broad family of metaheuristies Our stein fitness score may related to PDE-constrained swarm optimization (26).

---

Limitations
First, most adaptations to the diffusion process made in this paper are only valid in the context of EAI tasks. Indeed, genetic algorithms would not be suitable for image generation they drastically increase memory costs which are already an issue for high resolution image datasets. The tweaks analysed in Section 2.4 also not viable for image generation Song et al. [1I] show that reducing the number of inference steps largely decreases the performance of the models, and contrary to what we observe in our tasks, DDIM is actually the better option for fast image sampling. As already discussed in section 2.3 reducing the noise injection scales leads to unsatisfactory results for image generations.
Second, our genetic algorithm is the simplest possible since includes neither cross- boess-beeddig nor sophisticated mutations. Our metaheuristic is very simple and purely local. For instance, diversity control of swarm optimization [27 28 may be employed. Mutation and cross-breeding taken from image (non-diffusion) denoising may also be considered [29].
Third, there is a clear difference between robomimic and Adroit Hand tasks. Since the extrinsic dimension of the action space of the former is 3 times smaller than that of the latter, we hypothesize that robomimic tasks target distributions are significantly simpler than that of Adroit Hand. However, each robomimic task comprises more various manipulations suggesting more diverse conditioning. As result, little improvement may be achieved by improving the denoising process, the bottleneck expected to be the conditioning of the noise model cg. We did not test this hypothesis and our method cannot solve this problem.
Finally, our theoretical analysis remains preliminary. We conjecture that the expected denoising error can be bounded by an increasing function of the expected Stein score norm computed along the denoising trajectory. Moreover, a non-rigorous derivation suggests that, in the limit of an infinite population and infinitesimal step size, multinomial population denoising approximates the addition of a stochastic noise term together with a gradient-ascent term on the fitness score. To the best of our knowledge, the effect of noise-scale manipulation has not yet been studied from a theoretical standpoint. It is reasonable to expect that varying the noise scale preserves the support of the learned distribution while inducing bias toward its mean when the noise is reduced. Formal statements and proof's of these relationships are left for future work.
7 Conclusion
All in all our experiments demonstrate that using our proposed approach, improve performance as well as sampling rate of wettthhhelf-modeeee Additionally, using Genetic Denoising can help further improve model accuracy and stability, even with simple estimators of sample quality. We showed that off-the-shelf models can be used for two-steps inference with better performance compared shorteut models. We conclude that it is possible to exploit this type of inference framework to extract even more performance out of a given model provided the model well trained and the target distribution sophisticated enough.
References
[1] Yinchuan Li, Xinyu Shao, Jianping Zhang, Haozhi Wang, Leo Maxime Brunswic, Kaiwen Zhou, Jiqian Dong, Kaiyang Guo, Xiu Li, Zhitang Chen, et al. Generative models in decision making: A survey. arXiv preprint arXiv:2502.17100, 2025.
[2] Michael Janner, Yilun Du, Joshua B. Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In ICML, 2022.
[3] Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. The International Journal of Robotics Research, page 027836492412736688 2023.
[4] Hongjian Liu, Qingsong Xie, Tianxiang Ye, Zhijie Deng, Chen Chen, Shixiang Tang, Xueyang Fu, Haonan Lu, and Zheng-Jun Zha. Scott: Accelerating diffusion models with stochastic consistency distillation. In AAAI, 2025.

---

[5] Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. In ICML, 2023.
[6] Zehao Dou, Minshuo Chen, Mengdi Wang, and Zhuoran Yang. Theory consistency diffusion models: Distribution estimation meets fast sampling. In ICML, 2024.
[7] Kevin Frans, Danijar Hafner, Sergey Levine, and Pieter Abbeel. One step diffusion via shorteut models. In ICLR, 2025.
[8] Justin Fu, Aviral Kumar, Ofrr Nachum, George Tucker, and Sergey Levine. D4rl: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07211 2020.
[9] Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martin-Martin. What matters learning from offline human demonstrations for robot manipulation. In CoRL, 2021.
[10] Jonathan Ho. Ajay Jain, and Picter Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020.
[11] Jiaming Song. Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021.
[12] Tero Karras, Miia Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In NeurIPS, 2022.
[13] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In ICML, 2021.
[14] Zehua Chen, Xu Tan, Ke Wang. Shifeng Pan, Danilo Mandic, Lei He, and Sheng Zhao. Infergrad: Improving diffusion models for vocoder by considering inference in training. In ICASSP, 2022.
[15] Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein. The intrinsic dimension of images and its impact on learning. In ICLR, 2021.
[16] Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine. Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. In RSS.
[17] Real Stanford RL Lab. diffusion policy:: Diffusion policy for long-horizon manipulation. cow http://www...ooeeeeeeaaa//oaoonr
2023. Accessed: 2025-05-
16.
[18] Diederik Kingma and Jimmy Lei Ba. Adam: A method for stochastic gradient descent. In ICLR, 2015.
[19] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, In ICLR, 2019.
[20] Weijian Luo. A comprehensive survey on knowledge distillation of diffusion models. arXiv preprint 244442 2023.
[21] Amirmojtaba Sabour, Sanja Fidler, and Karsten Kreis. Align your steps: Optimizing sampling schedules in diffusion models. In ICML, 2024.
[22] Zhenyu Zhou, Defang Chen, Can Wang, and Chun Chen. Fast ode-based sampling for diffusion models in around steps. In CVPR, 2024.
[23] Haoxuan Chen, Yinuo Ren, Lexing Ying, and Grant M Rotskoff. Accelerating diffusion models with parallel sampling: Inference at sub-linear time complexity. In NeurlPS, 2024.
[24] Hui Huang, Jinniao Qiu, and Konstantin Riedl. On the global convergence of particle swarm optimization methods. Applied Mathematics & Optimizaation, 88(2):30, 2023.
[25] Fred W Glover and Gary Kochenberger. Handbook of metaheuristics, volume 57. Springer Science & Business Media, 2003.
10

---

[26] Carlo Sinigaglia, Andrea Manzoni, and Francesco Braghin. Density control of large-scale parti- cles swarm through pde-constrained optimization. IEEE Transactions on Roboties, 38(6):3530 3549, 2022.
[27] Maurice Clerc. Purticle swarm optimization, volume 93. John Wiley & Sons, 2010.
[28] Shi Cheng and Yuhui Shi. Diversity control in particle swarm optimization. In IEEE Symposium on Swarm Intelligence, 2011.
[29] Claudio FM Toledo, Lucas de Oliveira, Ricardo Dutra da Silva, and Helio Pedrini. Image denoising based on genetic algorithm. In IEEE Congress on Evolutionary Computation, 2013.
[30] Google Research. DDPM CelebA-HQ 256 x 256. httpps:/buugiiggaace ctt.eeeeooooggggooeeooo google/ ddpm celebahq-256 2022. Hugging Face model repository, accessed 2025-05--6
[31] John M Lee. Introduction to Riemannian manifolds, volume 2. Springer, 2018.
[32] Charles Fefferman, Sanjoy Mitter, and Hariharan Narayanan. Testing the manifold hypothesis. Journal of the American Mathematical Society, 2944):983-1049 2016.
[33] Pertti Mattila. Geometry of sets and measures in Euclidean spaces: fractals and rectifiability. volume 44. Cambridge University Press, 1999.
[34] Gerald A Edgar. Measure, topology, and fractal geometry, volume 2. Springer, 2008.
[35] Jonathan Weed and Francis Bach. Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance. Bernoulli, 25(44))2620-2648, 2019.
[36] Jan Stanezuk, Georgios Batzolis, Teo Deveney, and Carola-Bibiane Schönlieb. Your diffusion model secretly knows the dimension the data manifold, arXiv preprint arXiv:2212.26111, 2022.

---

A Extra Experimental Details
A.1 Image generation
In the Simple Empirical Solutions section, we show an image sample illustrating a mode collapse. This image was obtained using the poogle/ddpm--eeebbhh----- the associated scheduler to use 7 0, and run inference while setting the number of inference steps 50. Note that with the initial number of steps, the sample converges uniformly gray square.
pretrained pipeline (30]. We tweak
B Intrinsic Distribution Manifold Dimension Estimation
stating that data distributions supported by submanifold IR". The intrinsic dimension of a dataset refers to the minimum dimension of such manifolds supporting the whole dataset. Several notions of intrinsic dimension of dataset usually considered, the most common are based on Minkoswki or Hausdorff dimensions [33 34 Since work focuses diffusion models, favor the method proposed by Stanezuk et al. [36.. This methods allows to estimate the intrinsic dimension learned distribution of diffusion model by doing PCA of different values of the noise model 60-
We subscribe to the Manifold Hypothesis 31 32
35| I5
C Extra Experimental Results
C.1 Link between clipping and Score
25
76
b - 152
b 200
Figure 7: Same Figure but across action horizons h and timesteps t. Hue and marker styles also preserved.
12

---

C.2 DDIM
Normalized score
0.9
0.7-
0.6
0.00
0.02
0.04
0.06
0.08 0.10 0.12
Clipping frequency at t=
Figure 8: Same as Figure 3
Hue and marker styles are also preserved, with the added DDIM data
points in red
13

---

Table 2: Robomimic results. Normalized success rates over 500 seeds. PH and MH denote the train- ing dataset used for training: Proficient Human and Medium performing human. "DDPM+Schedule" refers to adapted schedule variant of DDPM. Notice that most success rates are within standard deviations of GDP suggesting that GDP fails to improve the base policy adapted schedule. We hypothesise that the quality of the base model is bottleneck as similar unclear results where observed Adroit when using less potent checkpoints.
Lift
Can
Square PH
Transport PH
ToolHang PH
Method
PH
MH
PH
MH
MH
MH
100-step inference
DDPM
1.00
1.00
0.97
0.95
0.92
0.85
0.84
0.62
0.53
DDPM
0
1.00
1.00
0.99
0.96
0.92
0.86
0.84
0.60
0.53
GDP
0.2
1.00
1.00
1.00
1.00
0.90
0.86
0.84
0.64
0.53
DDIM
0.998
0.998
0.982
0.970
0.928
0.846
0.846
0.606
0.514
5-step inference
DDPM
1.00
1.00
1.00
0.96
0.94
0.85
0.81
0.58
0.55
DDPM
0
1.00
0.99
0.99
0.97
0.92
0.85
0.83
0.61
0.52
GDP
0.2
1.00
1.00
0.99
0.97
0.95
0.86
0.77
0.59
0.50
DDIM
0.998
0.998
0.954
0.956
0.898
0.826
0.808
0.606
0.510
2-step inference
GDP
0.2
1.00
1.00
0.99
0.97
0.92
0.84
0.77
0.58
0.49
DDPM + Schedule
0
0.998
0.995
0.989
0.968
0.919
0.826
0.786
0.595
0.481
DDPM + Schedule 1
1
0.998
0.984
0.970
0.934
0.845
0.818
0.578
0.502
DDIM
0.998
1.000
0.982
0.966
0.922
0.842
0.821
0.604
0.480
Population NFE cost (us)
Step cost (us)
Overhead ratio
Notes
(DDPM)
3800
200
1.00
baseline
8
3800
500
1.08
under-utilized GPU
16
4000
800
1.20
32
4500
1500
1.50
64
5500
2400
1.98
memory-limited
Table 3: Inference wall-clock time comparison. The overhead ratio is the ratios of sum of NFE and step costs between population 1-64 (GDP) and population (DDPM).
14

---

Supplementary Materials
0.94
0.92
0.88-
0.86
0.84
Heuristic
Maximized Stein
0.82
Minimized
Vanilla
Solector
Multinominl
Noise injection factor
Figure 9: Normalized score across all noise injection factors, for 2-step diffusion. Results averaged over all Adroit tasks, with 20 trials of 100 environments each. The score function is used as the genetic algorithm heuristic. The selector picks using the given heuristic. Multinomial uses a temperature of 1, top & takes the best samples in sorted order the highest score sample is selected at O. We use population 16 on all runs.
Heuristic
Clip norm
0.90
Vanille
Minimized Clip Freq
Minimized Stein
0.85
Normalized
0.75
0.70
0.65
80
100
Steps
Figure 10: Normalized score across all numbers of steps, for y = 1, given different genetic algorithm selection heuristics. Results averaged over all Adroit tasks, with 20 trials of 100 environments each. We use a population on all runs.
15

---

Normalzrd cip Ever
0.00
0.00
0.08
Minimized Chp norm
Vanilln
Freq
Minimized Stain
Steps
Figure 11: Normalized clipping frequency across all numbers of steps, for y 1, given different genetic algorithm selection heuristics. Results averaged over all Adroit tasks, with 20 trials 100 environments each. We use population of 16 on all runs.
These figures show that minimizing the stein score (or the norm of the estimated noise) is the best of simple genetic algorithm heuristies. Using clipping statisties heuristics distorts the sampled distribution by removing the mode with large values, when the stein based heuristic only measures how out of distribution the current intermediary sample is.
Videos All gdp vidcos are generated using a genetic algorithm using 0.2, with stein score as heuristic, All non gdp videos videos are generated using vanilla policy using 7 For the 2-step diffusion, we use 80 as the maximum timestep and 20 as the minimum timestep.
16

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Section 2 and experiments provide backing for the reduction to 5 NFE while section and experiments provide backing for reduction to NFE.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long as is clear that these goals not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: Section 6 is dedicated Limitations.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
the paper. The authors should use their best
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
17

---

Justification:
We do not provide any new Theoretical results, our theory is backed by experiments as well as prior works.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: Experimental setting is detailed, we use standard diffusion libraries (Diffusers) and standard robotic task benchmarks (Adroit Hand and Robomimick) and datasets (D4RL).
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that appropriate to the research performed.
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
18

---

reproducible, they Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [No]
Justification: The work has been conducted under company policy preventing the authors from publishing codes in timeframe compatible with publication.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.co cc/
for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/
Cotee//ddeeeSbmmmcma ssi onPolicy) for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: The main body Experimental section provide key details. Most models were not even tuned, for more challenging tasks an optuna random search was performed
Guidelines:
The answer NA means that the paper does include experiments.
The experimental setting should be presented in the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer:
[Yes]
Justification: All plots generated using seaborn with confidence intervals.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
19

---

The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that out of range (e.g. negative еттоr rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of exccution) needed to reproduce the experiments?
Question:
Answer: [No]
Justification: The computational resources are not relevant for the point the article is making. The large body of experimental results used for our ablation study was obtained using small cluster of 3080 GPUs but reproduction may be done with personal ressources.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental rums as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer: [Yes]
Justification: All contributors involved are paid worker of the funding company. No human participant were used experiments, datasets used are public.
Guidelines:
The answer NA means that the authors have not reviewed the NeurlPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
Our work focused on accelerating and improving robotic manipulation effi- ciency of off-the-shelves models. As such, out work does not add societal consequences that were not already pre-existing in the potential harmful application of EAI, say military usage.
Justification:
Guidelines:
The answer NA means that there is no societal impact of the work performed.
20

---

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: No private data were used, no checkpoints are released since our work focused on improving off-the-shef models.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model. for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: Assets come from D4RL and Robomimics, both are cited as requested on their respective websites.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
21

---

For scraped data from a particular source (e.g., website). the copyright and terms of service of that souree should be provided.
assets are released, the license, copyright information, and terms use in the package should be provided. For popular datasets, paperswi thcode. com/ com/dataseta has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that re-packaged, both the original license and the license of the derived asset (if has changed) should be provided. If this information is not available online, the authors are encouraged to reach out to the asset's ' creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: No new assets are released.
Guidelines:
The answer NA means that the paper does release assets.
Researchers should communicate the details the dataset/code/mooel as part their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: No human subjects were used. The dataset used does include human made expert demonstrations, assume that D4RL and Robomimic did respect ethical of human expert.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included in the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: We did not perform research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
22

---

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if' applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: LLM were not used in this research except in used now common (text rephras- ing, English check, coding copilot).
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy (https://neurl)) hutt::///eerrrssccccoooofrrreer/ /0//////eeeeeeece//22////1/ for what should or should not be described.
23


Reviews:
[{"review_id": "2sLHhkZI3L", "reviewer": "Reviewer_PHeJ", "summary": "This paper investigates the dynamics of diffusion policy to optimize the sampling process. The authors find that by decreasing the noise injected + having a shorter denoising schedule, the sampling pipeline could be greatly accelerated. They also claim that poor performance is related to the clipping during the sampling process, and propose a genetic algorithm for sampling. They evaluated their hypothesis on various robotics tasks.", "questions": "1. For the experiments section, the paper lacks details in the experiment setup. For example, shortcut models require training, but no details were given in how the process was done.\n2. In the experiments section, what is\n    \n    > the the number of diffusion steps in {1,2,⋯,10} ∪ {20,30,⋯,100}\n    > \n    \n    Are many models being retrained with different numbers of diffusion steps? Or does this refer to the number of denoising steps, and the base model is always trained with 100 diffusion steps? Additionally, what denoising schedule is selected?\n    \n3. Why not include DDIM results in the experiments table? DDIM was included as part of the experiment, but the results (explicit scores) are never reported.\n4. For the Robomimic experiments, why are DDPM results missing in the last section, when the number of steps is 2?\n5. For the DDPM experiment entries, why is the noise injection only taken at extreme values (1 and 0)? what about the intermediate ones?\n6. For the proposed GDP method, there are two OoD score measurements proposed, which type did the experiments use?\n7. It’s unclear to me what figures 5 and 6 are depicting. The experiments have 4 dimensions (steps, noise scale, horizon, and method). Both of these are 2D graphs; what’s happening to the other two dimensions? Are they specific runs? Averaged over the other two dimensions?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n1. Proposes a new population-based algorithm for effective sampling under limited diffusion timesteps.\n2. Interesting finding that clipping leads to poor performance.\n\nWeakness:\n\n1. Experiments and methods are not clear. (see questions)\n2. Limited algorithm evaluation. Missing ablation studies to show the effectiveness of their method.", "was_revised": true, "final_justification": "My initial low score was due to the low quality presentation of the experiments, methods, and results. During the rebuttal period, most of the answers were addressed by the authors, and they promised to improve and address my concerns in the final version. Thus, I have increased my score on the paper.", "rebuttal": "We thank the reviewer for their constructive comments and insightful questions. The  requested experiments were omitted due to space limitation and the focus of the paper. However, we agree with the reviewer that they are valuable and therefore added them as requested (see Point 5).\n\n\n\n## 1. Experimental details - Shortcut model training\nWe re-implemented the shortcut model from Algorithm 1 in Frans et al., 2024 in PyTorch, using their official GitHub repository (which is in Jax). For each (task, horizon) pair, we trained 10 models via random hyperparameter search. Each reported score on a given task is the best-performing out of all 10 models. All models use the same 65M-parameter UNet backbone to ensure fair comparison.\n\n## 2. Models Retraining \n\nAll models were trained once using 100 denoising steps (128 for the shortcut model). At inference, we vary the number of denoising steps using the cosine schedule from Diffusion Policy. We also experimented with linear schedule and found that the impact of our method was similar but the performance of linear schedule models was significantly lower compared to cosine schedule both for baseline DDPM and GDP. We therefore decided to focus on cosine schedule.\n\n## 3. Clarification on Step Range in Experiments\n\nThe set {1, 2, ..., 10} ∪ {20, 30, ..., 100} refers to inference-time denoising steps only. All base models were trained using the full 100-step schedule (or 128 for shortcuts), and inference was conducted by subsampling the training schedule (Please refer to Equation (2) and lines 85–86 in the paper).\n\n## 4. DDIM results\n\nThe values for DDIM are compiled in the table below and integrated in tables 1 and 2 of the submission.\n|Name|100 steps|5 steps|2 steps|\n|----|----|----|----|\n|lift.ph|0.998|0.998|0.998|\n|lift.mh|0.998|0.998|1|\n|can.ph|0.982|0.954|0.982|\n|can.mh|0.97|0.956|0.966|\n|square.ph|0.928|0.898|0.922|\n|square.mh|0.846|0.826|0.842|\n|transport.ph|0.846|0.808|0.821|\n|transport.mh|0.606|0.606|0.604|\n|toolhang|0.514|0.51|0.48|\n|-|-|-|-|\n|Hammer|0.70|0.71|0.76|\n|Relocate|0.38|0.38|0.42|\n|Pen|0.50|0.70|0.73|\n|Door|0.83|0.81|0.95|\n\n## 5. Thorough ablation \n\nOur method modifies the denoising process in three ways: adapting inference schedule, reducing noise injection scale and genetic denoising.  For completeness, we added the missing lines to tables 1 and 2 of the submission.  For 2-step we provide the normalized scores for\n- DDPM, \n- DDPM + adapted schedule,\n- DDPM + adapted schedule + reduced noise,\n- DDPM + adapted schedule + reduced noise + GDP. \n\n2-step DDPM alone always yields minimal score as shown in the table below,\n\n| 2-step, adapted schedule    | gamma=0 | gamma=1 |\n|----|----|----|\n|Hammer|0.95|0.87|\n|Pen|0.75|0.74|\n|Relocate|0.74|0.64|\n|Door|1.0|0.97|\n| ------------ | ------------------ | ------------------|\n| lift.ph      | 0.998              | 0.998             |\n| lift.mh      | 0.995              | 0.998             |\n| can.ph       | 0.989              | 0.984             |\n| can.mh       | 0.968               | 0.970            |\n| square.ph    | 0.919              | 0.934             |\n| square.mh    | 0.826              | 0.845             |\n| transport.ph | 0.786              | 0.818             |\n| transport.mh | 0.595              | 0.578             |\n| toolhang     | 0.481              | 0.502             |\n\nNote that on Robomimic, the difference between $\\gamma=0$ and $\\gamma=1$ scores is not statistically signifiant as the difference is within 1-sigma.\n\n## 6. Noise injection scales\n\nFigure 3 and Appendix's Figure 9 include intermediate $\\gamma$ values. We chose not to include them in the tables for brevity, as $\\gamma= 0$ consistently yielded the best results.\n\n## 7.  Which OoD score was used for GdP experiments\n\nBoth Stein and clip-based scores were tested. The Stein score consistently performed better, both in reducing clipping and improving success rate. Thus, we report results using the Stein score.\n\n## 8.  Figure 5 and 6\n\nFigures 5 and 6 are averaged over the remaining two dimensions (e.g., horizons and tasks). We updated the captions to clarify this.\n\n## 9. Quality \nWe acknowledge that the current writing should be improved and we are working to improve writing quality and overall clarity."}, {"review_id": "s3aGwXZIET", "reviewer": "Reviewer_fdQZ", "summary": "This paper introduces the Genetic Diffusion Policy (GDP), a novel method to accelerate the inference of diffusion models for robotic manipulation tasks. The authors identify that standard diffusion policies, largely adapted from image generation, are inefficient for robotics due to a computationally expensive, multi-step denoising process. A key issue identified is \"clipping,\" a heuristic that forces predicted actions into a valid range but inadvertently pushes intermediate steps out-of-distribution (OoD), degrading performance and wasting iterations. By tailoring the denoising process to the low-dimensional nature of robotic action spaces, the authors show that performance can be improved with far fewer steps.\n\nTo address these challenges, GDP employs a population-based genetic algorithm during the denoising process. Instead of a single denoising path, GDP evolves a population of potential action sequences, using a fitness score to select for in-distribution trajectories and discard those prone to clipping errors. This genetic selection mechanism enhances both sample quality and stability, particularly for very low step counts. The method is evaluated on 14 tasks from D4RL and Robomimic, demonstrating that it can match or improve performance by up to 20% while reducing the number of required neural function evaluations to as few as two, significantly outperforming baselines like standard DDPM and Shortcut models.", "questions": "Overall, this is a promising paper with strong empirical results. The core argument regarding clipping-induced denoising defects is well-supported by the experiments and provides a clear, intuitive explanation for performance degradation in standard diffusion policies. The proposed Genetic Diffusion Policy (GDP) demonstrates impressive performance, especially in the two-step inference regime. However, the paper could be strengthened by addressing the following points:\n\n**1. Clarification on Baselines and Comparisons:**\n\n* **DDIM as a Benchmark:** While the paper mentions that DDIM did not perform well in experiments, it is a standard baseline for fast sampling. For a more complete comparison, could the authors include DDIM's performance in the main results tables (e.g., Table 1), even if it is suboptimal? This would provide readers with a direct comparison against a widely-known method.\n* **Shortcut Model Analysis:** The Shortcut model is presented as a key competitor.\n    * Does the noise scaling factor (`γ`) apply to the Shortcut model's inference, or is it only tunable for DDPM and GDP?\n    * Figure 8 shows that the Shortcut model's performance is robust across different iteration steps but significantly lower than GDP's. Could the authors provide more insight into why this might be the case?\n    * Was the Shortcut model benchmarked on the Robomimic tasks? The results are shown for Adroit Hand tasks but seem to be missing for Robomimic.\n\n**2. Deeper Explanation of Key Concepts:**\n\n* **The Horizon Tradeoff:** The authors hypothesize that intermediate horizons are more difficult due to a tradeoff between \"conditioning complexity\" and \"distribution complexity\". This is an interesting point that would benefit from more detail. Could the authors elaborate on this concept? For example, what defines these two complexities in the context of the robotic tasks, and how does their interplay lead to the observed performance dip?\n* **The Role of the Genetic Algorithm:** The \"Genetic Denoising\" process is central to the paper's contribution. However, the current implementation consists of selection and duplication, without crossover or mutation. To better isolate the benefit of the population-based approach, it would be helpful to understand its specific role. How does multinomial selection and duplication of a population compare to a simpler heuristic, such as calculating the fitness score for all samples, selecting the single best one, and proceeding with only that trajectory? An ablation study on this point would clarify whether the population diversity itself is critical, or if the main benefit comes from simply identifying and repeating a single \"good\" sample.", "limitations": "### Scope and Generalizability\n\nThe most significant limitation is that the method's core principles are fundamentally tied to the specific characteristics of **low-dimensional robotic action spaces**. The very premise that reducing noise and inference steps is beneficial runs counter to findings in high-dimensional domains like image generation. This confines the paper's impact, making it a specialized solution for Embodied AI rather than a general advancement for diffusion models.\n\nThis limitation extends to scalability within robotics itself. The experiments are conducted on tasks with action spaces of around 30 dimensions. It remains an open question how GDP would perform on more complex robotic platforms, such as humanoids, where action spaces can involve hundreds of dimensions. As dimensionality increases, the \"curse of dimensionality\" could render the fitness heuristics less effective and the population-based search far less efficient, potentially reintroducing the very performance issues the method aims to solve.\n\n\n### Practical and Computational Considerations\n\nWhile the paper focuses on reducing the number of neural function evaluations (NFE) to lower latency, it doesn't fully address the **overall computational overhead** of GDP.\n* **Memory and Parallelism:** Maintaining a population of `P` samples increases memory usage by a factor of `P`. More importantly, it requires `P` parallel evaluations of the noise model at each step before selection can occur. The total wall-clock time of a 2-step GDP with a population of 16 or 32 might be comparable to or even exceed that of a standard 5 or 10-step DDPM on certain hardware. This practical tradeoff is not quantified.\n* **Hyperparameter Sensitivity:** GDP introduces several new, potentially sensitive hyperparameters, including population size, survival number, and the fitness temperature `T`. The paper mentions using a \"coarse parameter grid,\" which suggests that finding an optimal configuration for a new task may require a non-trivial and expensive tuning process. A robust method should ideally be less sensitive to such additions.\n\n---\n\n### Methodological Dependencies\n\nThe success of GDP is critically dependent on two external factors that limit its use as a \"plug-and-play\" solution.\n1.  **Reliance on a Strong Base Model:** The method is not a panacea for poor policies. The authors note it performs best when the underlying diffusion model is already well-trained and the task is sufficiently complex. This means GDP acts as an **optimizer for an already competent policy**, not a tool to salvage a poorly trained one. It cannot fix fundamental issues in the model's ability to understand the environment from its observations.\n2.  **Dependence on the Fitness Heuristic:** The entire genetic selection process hinges on the assumption that the fitness score (e.g., `φ_clip`) is a reliable proxy for sample quality. While the clipping-based score is intuitive and works well for the tasks presented, it is still a heuristic. For different robot morphologies, task objectives, or action representations, this heuristic might fail. If the fitness score does not accurately reflect proximity to the true data manifold, the genetic selection could be ineffective or even detrimental, actively selecting for suboptimal trajectories. The method's success is therefore fundamentally coupled to the quality of this handcrafted heuristic.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths of the Paper\n\n* **Insightful Problem Formulation and Diagnosis:** The paper's primary strength is its clear diagnosis of a core issue plaguing diffusion policies in robotics: **clipping-induced denoising defects**. Instead of just proposing a faster method, the authors identify a root cause—the distributional mismatch created by clipping early in the denoising process—and build their solution around it. The direct correlation shown between clipping frequency and poor performance provides strong, intuitive evidence for their central claim.\n* **Comprehensive and Rigorous Empirical Evaluation:** The claims are supported by an extensive set of experiments. The evaluation spans 14 different tasks across two major benchmarks (D4RL and Robomimic), multiple action horizons, and a wide range of inference budgets. The scale of the evaluation, with over 2 million runs and up to 500 seeds for some tasks, lends significant statistical weight to the findings and demonstrates the method's robustness.\n* **Practicality and Impact on Off-the-Shelf Models:** A major advantage of the proposed approach is its practicality. The Genetic Diffusion Policy (GDP) and the simpler tweaks (reducing noise, adjusting the schedule) can be applied to **existing, pre-trained diffusion policies without any architectural changes or retraining**. This significantly lowers the barrier to adoption and means that performance gains can be realized on already-developed models, as demonstrated by their 2-step GDP outperforming fully iterated baselines.\n* **Novelty of the Genetic Denoising Approach:** The paper introduces a novel application of a population-based sampling strategy to accelerate diffusion models. While genetic algorithms are well-established, their use as a selection mechanism within the denoising process to filter trajectories based on an OoD score is a creative and previously unexplored idea in this context. This opens a new research direction for improving diffusion samplers.\n\n### Weaknesses and Opportunities for Improvement\n\n* **Depth of the \"Genetic\" Contribution:** The term \"Genetic Diffusion Policy\" may slightly oversell the mechanism, which currently consists of **selection and duplication without the canonical genetic operators of crossover and mutation**. The paper does not provide an ablation study to demonstrate that the population-based multinomial selection is superior to a simpler heuristic (e.g., a greedy approach that selects the single \"best\" trajectory at each step and proceeds). Such an analysis would be crucial to justify the additional computational cost of maintaining a population and to clarify the specific role that population diversity plays in the final performance.\n* **Nuances in Baseline Comparisons:** The treatment of established baselines could be more thorough.\n    * **DDIM:** This model is a cornerstone of fast diffusion sampling but is dismissed relatively quickly in the text as not performing well. For a more complete scientific record, including DDIM's scores in the main results tables would provide a valuable, direct comparison for the reader, even if the performance is poor.\n    * **Shortcut Models:** The analysis of Shortcut models feels incomplete. Their performance is shown to be stable but suboptimal, yet the reason for this is not deeply explored. Furthermore, the comparison is only performed on the Adroit Hand tasks, leaving an open question about how GDP compares to Shortcut on the Robomimic benchmark.\n* **Under-Explored Hypotheses:** The paper presents several interesting hypotheses that are not fully substantiated. The most notable is the \"horizon issue,\" where the authors suggest performance dips at intermediate horizons due to a tradeoff between conditioning complexity and distribution complexity. While plausible, this is presented as speculation without supporting analysis. The paper would be stronger if this claim were either supported with further evidence or explicitly framed as a direction for future work.\n* **Generalizability Across Task Types:** The paper notes a clear difference in performance and improvement margins between the Adroit Hand and Robomimic tasks, hypothesizing this is due to the latter being more bottlenecked by the model's conditioning rather than the denoising process. This is a key point regarding the limitations and applicability of GDP. Exploring this difference further would provide valuable insight into which types of robotic manipulation problems stand to benefit most from this and similar denoising-focused improvements.", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the thoughtful and constructive feedback. We appreciate the precision provided and the points raised. Below, we address the reviewer’s concerns in detail.\n\n---\n\n## 1. Weaknesses and Clarifications\n\n### Generalizability Across Task Types\n\nWe agree with the reviewer’s observation. Our intuition is that for tasks like those from Robomimic, the conditional action distributions are simpler and more localized. As our method enhances the denoising process but does not modify the base model, its benefit is limited when the target distribution is easily covered, even with a few steps. We infer that the limiting factor for the Robomimic tasks is a conditioning bottleneck.  The impact of out-of-distribution (OoD) trajectories is then smaller, leading to limited performance gains of our method despite latency reduction. While we did not formally validate this hypothesis, it is consistent with the empirical trends observed across all Robomimic tasks.\n\n---\n\n## 2. Baseline Comparisons\n\n### DDIM Scores\n\nWe added the DDIM results, see point 4 of the rebuttal to reviewer PHeJ. \n\n### Shortcut Model and γ\n\nShortcut is a self-consistency variant of Flow-Matching (FM), as such it does not inject noise during inference. Stochasticity only comes from the choice of starting point in latent space. The $\\gamma$ parameter has no direct equivalent but may be related to the standard deviation of the latent space normal distribution during inference. We did not experiment with this possibility.\nGuo et al.'s \"Variational Rectified Flow Matching\" introduces stochasticity along the inference process via VAE step-model; however, we did not find an adequate equivalent to $\\gamma$. Finally, combining both self-consistency (shortcut FM) and distributional step-model (variational FM) would likely motivate a publication on its own.\n\n### Shortcut Performance on Adroit\n\nShortcut models require the network to learn both a vector field and its integral. With a fixed architecture (same UNet backbone), this leads to capacity bottlenecks that likely explain the inferior performance observed. \n\n### Shortcut on Robomimic\n\nWe agree that comparing the shortcut with the 2-step GDP is relevant for Robomimic tasks; however, no shortcut model checkpoints were publicly available for Robomimic tasks, so the comparison would be unfair to the off-the-shelf diffusion models we benchmarked.\n\n---\n\n## 3. Conceptual Clarifications\n\n\n### Horizon Tradeoff Hypothesis\n\nThank you for highlighting this point. A thorough analysis of the horizon-dependence was initially intended to be included in the present work. However, we concluded that a proper treatment reveals deeper phenomena that merit a separate follow-up article. Below, we summarize our current insights.\n\nEmpirically, we observe three distinct regimes based on the action horizon $ \\mathbf{h_\\mathcal{A}} $:\n\n- $ \\mathbf{h_\\mathcal{A}} \\leq 5 $: **Low performance**\n- $ 24 \\leq \\mathbf{h_\\mathcal{A}} \\leq 50$: **High performance, then decreasing**\n- $ \\mathbf{h_\\mathcal{A}} \\geq 100 $: **High performance resumes**\n\nWe hypothesize that these regimes arise from competing factors:\n\n1. **For very short horizons** (< 5): the action distributions are heavily overlapping and \"blob-like\", leading to mode confusion. The model struggles to disambiguate modes due to the compressed embedding space. Increasing the horizon expands the dimensionality of the action sequence, which helps disambiguate modes and better leverage conditioning.\n\n2. **Distribution Complexity**: As the horizon increases, the model must represent more complex and multi-modal action distributions. Fortunately, diffusion models are expressive enough to accommodate this growing complexity.\n\n3. **Conditioning Complexity**: Shorter horizons also require the model to infer contextual information (e.g., temporal position) from the observation, which makes conditioning harder. For example, in short-horizon settings, the model must deduce which timestep is being referenced based solely on the observation. In contrast, long-horizon models can typically assume that the observation corresponds to the first timestep.\n\nHence, performance is governed by a trade-off between **distribution complexity** (which increases with horizon) and **conditioning complexity** (which decreases with horizon). We believe that the performance drop observed in the intermediate horizon regime $ 50 \\leq \\mathbf{h_\\mathcal{A}} \\leq 100 $ results from both complexities being simultaneously high.\n\nTo further analyze this phenomenon, we performed a preliminary investigation of the structure of the learned action distributions. Specifically, we estimated:\n- the **intrinsic manifold dimension**,\n- the **intrinsic linear dimension**, and\n- the **number of distributional modes**.\n\nThe ratio between the intrinsic linear and manifold dimensions offers a geometric measure of complexity, which complements standard mode-counting approaches.\n\nLooking forward, we aim to explore more principled **Wasserstein-based complexity measures**, which can capture both the shape and spread of distributions:\n- **Expected Wasserstein Distance to Gaussian** (as a proxy for distributional complexity),\n- **Wasserstein Barycenter Distance distance to Gaussian** (another proxy for distribution already complexity), and\n- **Wasserstein Variance** (quantifying the distributionnal diversity across conditioning).\n\n\n\n\n### Role of the Genetic Algorithm\n\nOur goal was to validate the utility of population-based denoising without introducing algorithmic complexity. We did run comparative experiments\n Top-k vs. Multinomial Selection: At 2 steps, top-k outperforms multinomial slightly. However, it quickly collapses diversity in multi-step settings, leading to mode collapse.\n\nWhile a greedy selection heuristic could achieve similar performance in the very low-step regime, it would fail to scale beyond that. The population-based design is essential for maintaining robustness as the denoising depth increases. \nIf diversity is what the current task requires, more sophisticated population-based metaheuristics like\nParticle Swarm Optimization or Ant Colony Optimization might be better suited and could be applied, but we leave such an exploration to future work. \n\n---\n\n## 4. Practical and Computational Considerations\n\n### Memory and Wall-Clock Cost\n\nWe measured the step-wise overhead for GDP using a standard RTX 3080 GPU without specific optimization beyond calling the model batch-wise on the population.\n\n|Pop. Size |NFE cost ($\\mu$s)| Step cost  ($\\mu$s)|  Overhead ratio|\n|:----:|:----:|:----:|:----:|\n|1 (DDPM)|3800|200|1|\n|8|3800|500|1.075|\n|16|4000|800|1.2|\n|32|4500|1500|1.5|b\n|64|5500|2400|1.975|\n\nThe relatively low cost is due to the under-utilization of the GPU computation and memory. As the model grows bigger, the population may be more constrained. We argue the following.\n- In production, the observation embedding may represent a significant portion of the computational budget but since it can be shared across the population, its impact on the overhead is limited.\n- Training is typically done with a batch size larger than one; thus, assuming training and inference are done on the same hardware, inference is likely to leave room for a non-trivial population.\n- Fully-fused models, weight quantization, and other low-level optimization would allow for a larger batch size (hence a population) during inference.\n\nThese points will be discussed in a dedicated section in the appendix.\n\n\n### Hyperparameter Sensitivity\n\nOur coarse grid search served both as an ablation and robustness check. We found GDP to be relatively insensitive to population sizes in [8, 32] and temperature T in [1, 1000]. Increasing temperatures caused performance to vary from optimal at low temperatures to vanilla DDPM at high temperatures. While additional hyperparameters are introduced, we show that good performance is obtainable across a wide range without fine-tuning. That said, we acknowledge that future work should explore more principled tuning strategies or adaptive methods.\n\n---\n\n## 5. Limitations\n\n### Dependence on Fitness Score\n\nIndeed, the success of GDP depends on the choice of fitness function. For robotic tasks with saturation constraints, our clipping-based score is effective. For tasks where clipping is rare (e.g., end-effector control, image domains), this heuristic is less useful. Still, whatever the task, Stein-based scores are always available and valid candidates. Furthermore, we note that it may be possible to learn a task-specific fitness function in a supervised or self-supervised manner, which is a promising direction for future work.\n\n### Scalability to High-Dimensional Action Spaces\n\nWhile our action spaces have ~30 DoFs, our experiments span horizons up to 200, leading to effective dimensions over 6000. This is higher than image datasets like CIFAR. Furthermore, many humanoid benchmarks such as HumanoidBench have ≤61 DoFs, keeping GDP within practical scaling bounds for whole-body control tasks.\n\nWe will note this point in the limitations.\n\n\n## 6. Quality \n We will conduct a thorough proofreading and will improve the overall writing quality and clarity of our paper."}, {"review_id": "ge3UjLBPBV", "reviewer": "Reviewer_UvdB", "summary": "The authors propose a new population-based sampling strategy for denoising process in embodied AI tasks. The method filter denoising samples based on an OOD score to reduce the injected noise when denoising. The authors show improvement on both score and clipping frequency using several diffusion models.", "questions": "1. One of the major problem is: The clip operation in Eq.2 is the foundation of many following analysis. But I’m not sure whether this clipping is necessary for diffusion policy / flow-matching policy. Especially, for middle $t$, is it necessary to keep the clipped item between the threshold? I’ll be interesting to see some analysis like Fig3 but without clipping. How will the raw diffusion policy perform? Will the proposed method still work?\n\n2. For results in Figure 3, it seems like less noise scale will in general leads to better score. Those yellow points with 0 noise injection scale usually yield best results, which seems no tradeoff at all. Is it still necessary to use flexible stochastic modeling method like diffusion model to learn the policy? Or it will be enough to learn through some deterministic policy network?\n\n3. Can the authors further explain the \"tradeoff conditioning complexity for distribution complexity\" in L256?\n\n4. It would be better to have some theoretical analysis.", "limitations": "N/A", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- In general, I like the observations and analysis as illustrated in Section 2. It has an interesting topic with a clean method which is verified on several different tasks.\n\n- The method itself is simple and seems to be helpful, which help people to gain more insights on what's going on on the diffusion policy.\n\nWeaknesses:\n\n- Currently, the analysis and some claims are somehow seem to be subjective and intuitive. It would be helpful if the authors could provide some theoretical analysis for this method.\n\n- See questions below.", "was_revised": true, "final_justification": "This paper provides an interesting view into diffusion process and seems this method is able to serve as a trick to apply to different tasks.", "rebuttal": "We thank the reviewer for their comments. We appreciate that they find the topic interesting and our method well established. Below we address the points raised in the review.\n\n## 1. Necessity of Clipping in Diffusion\n\nEach DDPM denoising step predicts a fully **denoised** sample, which is then re-noised at a lower noise level. Clipping is applied only to this fully denoised sample prior to re-noising. Its purpose is twofold: to enforce bounded action constraints and to avoid numerical instabilities at high noise levels—particularly with schedules like cosine. From this perspective, clipping may be viewed as a form of *universal guidance* (Bansal et al. \"Universal Guidance for Diffusion Models\") with a \"potential well\" guidance.\n\nWhile such clipping is helpful in standard DDPM settings, alternative noise parameterizations, such as the variance-exploding linear schedule used in EDM, are less prone to numerical instabilities. Notably, EDM avoids rescaling the original sample, ensuring that predictions remain within the convex hull of the data distribution. Similarly, flow-matching models inherently satisfy this constraint and do not require clipping.\n\nAlthough we considered a *clipping schedule* (loosening the constraint over time), we ultimately abandoned it: clipping is already a heuristic justified primarily by empirical results, and further engineering on top of it lacked theoretical grounding. Instead, our approach offers a principled solution by improving alignment between the denoising trajectory and the data distribution, without compounding ad-hoc mechanisms.\n\n---\n\n## 2. Is a Stochastic Policy Still Necessary if γ = 0 Performs Best?\n\nWhile the denoising process may become deterministic as γ → 0, the policy remains stochastic due to the random initialization from Gaussian noise. This setting mirrors standard DDIM and Flow Matching sampling: randomness exists only at the start, and is then followed by a deterministic process. \n\nMoreover, stochasticity is essential *during training*, even if the final policy is effectively deterministic at inference time. Stochastic training encourages *mode coverage*, which helps prevent mode collapse—a critical concern in robotics. For example, in a 2D pick-and-place task with obstacles, multiple disjoint paths may exist. If the obstacle changes location, a deterministic policy may fail catastrophically if it relies on a single, now-infeasible mode. A stochastic policy can maintain robustness by supporting multiple valid behaviors, from which guidance or downstream selection mechanisms can choose.\n\n---\n\n## 3. Tradeoff Between Conditioning Complexity and Distribution Complexity (L256)\n\nWe address this fully in our response to Reviewer fdQZ, Point 2, under the \"Horizon Tradeoff Hypothesis.\" In brief: shorter horizons simplify the output distribution but complicate conditioning (as the model must infer context from a single observation). Longer horizons ease conditioning (since the observation can often be assumed to be the initial state) but require modeling a more complex multi-step distribution. The intermediate horizon regime thus suffers from the compounded difficulties of both.\n\n---\n\n## 4. On the Lack of Theoretical Analysis\n\nWe acknowledge the value of theory and are actively working toward formal guarantees. A first result we aim to establish is a bound of the form:\n\n$$\n\\mathbb{E}_{t, x_t} \\left( \\left. \\| \\epsilon^\\theta(t, x_t) - \\epsilon(t, x_t) \\|  \\right|  \\| \\epsilon(t, x_t) \\| \\right) \\leq C(\\|\\epsilon\\|) \\cdot \\mathcal{L},\n$$\n\nwhere $ \\mathcal{L} $ is the training loss and $ C(\\|\\epsilon\\|) $ is an explicit *increasing* function vanishing at zero. This would characterize how the denoising error increases with noise magnitude, supporting the intuition that OoD predictions at high noise lead to degraded performance.  Our goal is to include the full results in the paper.\n\nA more ambitious goal is to relate the expected sample density under the learned distribution to the Stein score norm, potentially bounding it from below by a decreasing function of the score norm. This remains speculative, but we are exploring this direction.\n\n## 5. Quality \nWe will conduct a thorough proofreading and will improve the overall writing quality and clarity of our paper."}, {"review_id": "P5huM2cXSY", "reviewer": "Reviewer_ptGF", "summary": "This paper presents an inference-time acceleration method for diffusion policies in robotic manipulation, leveraging a population-based strategy termed Genetic Denoising. The key insight is that the standard diffusion denoising process is often inefficient and overkill for low-dimensional action spaces in control tasks. The authors propose reducing the number of neural evaluations during inference to as few as two. They introduce a selection-based denoising scheme, where candidate trajectories are scored and evolved to minimize out-of-distribution risk. The method is evaluated across 14 D4RL and Robomimic tasks, showing improved or comparable performance to baselines, while significantly reducing compute.", "questions": "- What's the actual inference runtime for the method? Does the trajectory selection (scoring, sorting) process introduce overhead? What are the runtime comparisons with other baselines?\n- Can the method scale to tasks with higher-dimensional actions (e.g., 30-DoF humanoid or dexterous hand control)? How does the performance degrade with action space size?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n- The paper proposes a novel genetic denoising approach - using a population-based search to select good candidates based on ood scores. This approach allows using much fewer denoising steps.\n- The paper highlights that in low-dimensional action spaces, the final denoised sample is often recoverable from early steps. This motivates the reduced-step formulation.\n- The method does not require retraining the diffusion policy, making it a practical and cheap improvement to existing models.\n\nWeakness\n- Despite extensive simulation results, there are no real-world robot experiments to verify that the proposed fast denoising retains robustness when deployed on hardware. Real-world experiments could also show the benefits of faster inference by allowing dynamic manipulation tasks.\n- The paper argues that mode collapse is not a problem in robotics tasks. However, multi-modal action distributions can be valuable, especially for ambiguous goals or multi-task policies. The value of sampling diversity at inference time (e.g., for steering or preference selection) is neglected.\n- More analysis and theoretical justification on when the method is robust under heavy step reduction and when the method might fail (e.g., under high stochasticity or noisy dynamics) would be helpful.", "was_revised": true, "final_justification": "My concerns have been addressed during rebuttal and I lean towards accepting the paper.", "rebuttal": "We thank the reviewer for their feedback.  We are pleased that they find our method well-motivated and practical. We respond to their comments and questions below.\n\n## 1. Inference runtime\n(same answer as to reviewer fdQZ)\nWe measured the step-wise overhead for GDP using a standard RTX 3080 GPU without specific optimization beyond calling the model batch-wise on the population.\n\n|Pop. Size |NFE cost ($\\mu$s)| Step cost  ($\\mu$s)|  Overhead ratio|\n|:----:|:----:|:----:|:----:|\n|1 (DDPM)|3800|200|1|\n|8|3800|500|1.075|\n|16|4000|800|1.2|\n|32|4500|1500|1.5|b\n|64|5500|2400|1.975|\n\nThe relatively low cost is due to the under-utilization of the GPU computation and memory abilities. As the model grows bigger, the population may be more constrained. We argue the following.\n- In production, the observation embedding may represent a significant portion of the computational budget but since it can be shared across the population, its impact on the overhead is limited.\n- Training is typically done with a batch size larger than one; thus, assuming training and inference are done on the same hardware, inference is likely to leave room for a non-trivial population.\n- Fully-fused models, weight quantization, and other low-level optimization would allow for a larger batch size (hence a population) during inference.\n\nThese points will be discussed in a dedicated section in the appendix.\n\n## 2 . Method Scalability\n\nOur experiments include Robomimic and Adroit, which span low to medium action dimensionality, 9 and up to 30 DoF respectively. Results indicate that the method scales well and tends to be more beneficial in complex tasks, particularly those with more convoluted action distributions,  even at higher prediction horizons. As noted in answer to reviewer fdQZ, despite the low action space dimensionality, our experiments with long horizons show the method is efficient for extrinsic distribution dimension comparable to CIFAR or ImageNet-64.\n\nWe have not tested the method on humanoid-level tasks (~60 DoF). Still, the demonstrated scalability up to 30 DoF and 200-step horizons suggests that the method would extend to such domains, at least for short horizons.\n\n## 3. Real world experiment\nWe tested our method on a pick-and-place task using a Franka Emika Panda arm. All methods—DDPM with 100 steps, 5 steps, 2 steps (with adapted schedules and zero variance), and GDP—achieved a 100% success rate. Although this does not highlight performance differences, it demonstrates that our approach is robust and deployable on real hardware for such tasks. Results and experimental details will be included in the paper.\n\nHowever, showcasing dynamic benefits from faster inference in real-world manipulation remains impractical for now. Such demonstrations would require a robotic platform capable of ultra-fast closed-loop control and streamlined integration between perception, model inference, and actuation, which are beyond our current resources.\n\nNevertheless, we argue that reducing inference cost provides real benefits:\n- Enabling more reactive or frequent control cycles;\n- Freeing GPU compute for parallel tasks (e.g., perception or planning);\n- Allowing deeper models.\n- Allowing deployment on less expensive hardware.\n\n\n## 4. Mode Collapse \n\nWe agree that mode collapse may harm sample diversity, particularly in guided generation. We also acknowledge that guidance effectiveness typically declines as the number of denoising steps is reduced.\n\nOur proposed mechanism enables a diversity trade-off: possibly lowering the diversity with $ \\gamma $ while compensating with a larger population. It is important to note that lowering $\\gamma$ distorts the generated distribution, but it does not necessarily reduce mode coverage, just like increasing it does not necessarily increase mode coverage.\n\nIn future work, we intend to explore a more integrated use of guidance with genetic denoising. For instance, applying guidance before selection and then scoring candidates with a Stein metric may preserve distributional support while promoting goal satisfaction—even with few denoising steps.\n\n## 5. Theoretical Contribution\n\nThe theoretical motivation for Stein score-based selection is detailed in our response to Reviewer UvdB, point 4.\n\nWe also see promising directions for further theoretical development. One avenue is to formalize the diversity gain induced by our population mechanism. We conjecture that in the limit of many denoising steps, population-based selection approximates the effect of increasing the noise level $ \\gamma $, thus promoting broader exploration. A formal proof of this claim is left for future work.\n\nTo the best of our knowledge, there is no theoretical description of the noise injection scale distortion in the current literature. Partial results are likely achievable in the coming week such as distribution support preservation using the line of argumentation of Pidstrigach's \"Score-Based Generative Models Detect Manifolds\".\n\n## 6. Quality \nWe will conduct a thorough proofreading and will improve the overall writing quality and clarity of our paper."}]

Author rebuttal:

Meta-review: This paper proposes a population-based denoising strategy -- in this case a genetic algorithm -- to generate actions for robotic manipulation tasks. The authors identify a key failure mode of action generation -- clipping -- and propose their population-based method as a successful fix, achieving improved performance in D4RL and Robomimic. The reviewers found several shortcomings in the original draft -- notably on the lack of support for some of the story and missing ablations -- but these have been well-addressed in the rebuttal discussion. 

I therefore recommend the paper is accepted as a poster.
Decision: Accept (poster)