Title: Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Venue: NeurIPS 2025
Accepted: yes
Keywords: diffusion models

shortcut models

fast diffusion

clipping

DDPM

diffusion policy

robotic manipulation

embodied AI

Unet

metaheuristic

genetic algorithm

D4RL

robomimic
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

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.

1 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 [1]. These challenges are further compounded by data scarcity and

the need for strong generalization from limited demonstrations. Diffusion-based policies [2, 3] offer a promising solution by learning to model the full distribution over expert actions, thereby mitigating mode collapse and enabling diverse robust behavior.

Despite their success, diffusion models suffer from a key drawback: inference is sequential and computationally expensive, requiring many denoising steps to produce high-quality samples. This latency is a major limitation for real-time applications in robotics, where fast and reliable action generation is critical. To address this, recent works have proposed distillation [4], consistency models [5, 6], and shortcut flow-matching [7], which trade off simplicity or performance for faster sampling by training new models.

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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 out-of-distribution (OoD) intermediate states caused by clipping-a heuristic introduced 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 AI. Rather than blindly adopting heuristics from vision, robotic policy models require a dedicated analysis of their training dynamics, action spaces, and inference behavior.

To this end, we introduce the Genetic Diffusion Policy (GDP), see Figure 2, which uses a populationbased selection mechanism to filter denoising trajectories based on an OoD score, reducing clipping artifacts and improving sample quality-especially at low step counts.

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We evaluate our method on 14 manipulation tasks from D4RL [8] 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 [12], and shortcut diffusion models [7]. Figure 1 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 in low-dimensional robotic settings.

Genetic Diffusion Policy (GDP): We introduce a novel sampling strategy that uses a population 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 at employing genetic algorithms to accelerate diffusion model sampling.

2 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, π ⟩ . Here S denotes the set of environment states, O the set of observations, A the action space, T ∶ S × A→S the (possibly stochastic) transition kernel, and R ∶S→ R ≥ 0 a terminalreward function. At time t the physical world-including the robot-is in some s t ∈ S , while the agent only perceives a partial observation o t ∈O and selects an action a t ∈A according to a policy π ∶O→A , after which the environment transitions to s t + 1 ∼ T ( s t , a t ) .

Training. Diffusion policies [2, 3] adapt the diffusion-model paradigm [10, 11] to decision making. Given a demonstration data set D = {( o ( i ) t , a ( i ) t ) t ∗ t = 0 } ∣D∣ i = 1 comprising successful episodes, we construct a distribution i.e. windows of length h O past observations and h A future actions centered at some τ . For numerical stability, we assume A is embedded in R d and rescaled to [-1 , 1 ] d .

For a chosen noise schedule ( α t ) T t = 0 , the network ϵ θ ∶ A h A ×{ 0 , /uni22EF , T }×O h O →A h A is trained by minimizing the Denoising Diffusion Probabilistic Model (DDPM) loss

Inference. At test time we denoise an initial x T ∼N( 0 , I ) using

where σ 2 t ∶= η 2 ( 1 -α t -1 )( α t -1 -α t ) α t -1 ( 1 -α t ) , η ∈ [ 0 , 1 ] interpolates between the deterministic DDIM sampler ( η = 0 ) and the stochastic DDPM sampler ( η = 1 ), and γ = 1 in the standard formulation. Equation (2) is a finite-difference discretizations of the Stochastic Differential Equation (SDE) derived in [12],

coupling the probability-flow ODE with a Langevin diffusion. Separating the training horizon T from a possibly reduced number of inference steps δ is straightforward: replace ( t, t -1 ) in (2) and in σ t by ( t j , t j -1 ) for a monotone schedule 0 ≤ t 0 < ⋅ ⋅ ⋅ < t δ ≤ T .

2.2 Clipping-Induced Denoising Defects

The clip operation in (2) serves two independent purposes. First, robotic actions are normalized to [-1 , 1 ] d , so any estimator ˆ x 0 lying outside that cube must be projected back. Second, for the first few timesteps of a cosine noise schedule [13] we have α t ≈ 0 , making the denominator √ α t ill-conditioned; clipping prevents numerical explosions.

Unfortunately, this crude safeguard creates a subtle distributional mismatch. Near t = T most coordinates of ˆ x 0 saturate at {-1 , 1 } , so the inference distribution x t ∼ 1 σ (N( 0 , σ 2 I ) ⊗ ( ˆ µ ∣ o )) is supported almost exclusively on the corners of the hyper-cube, whereas training sees N( 0 , σ 2 I )⊗( µ ∣ o ) , whose conditional µ is spread throughout the interior. Consequently,

early denoising steps convey little task-relevant signal;

the predictor ϵ θ incurs larger errors (because it never learned to handle these extreme inputs);

the overall reverse process wastes iterations before trajectories re-enter the high-density region of the noised target action manifold.

Figure 3 quantifies this phenomenon across tasks and sampler hyper-parameters: the higher the clipping frequency, the lower the final episodic return. Similar training-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-Exploitation 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 a 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 η and γ allow an exploration-exploitation trade-off that one may leverage to mitigate the clipping issue. As discussed in the introduction, generalization requires to train a stochastic policy to mitigate mode collapse during training. However, mode collapse is not an issue during inference: a robot that always chooses the same solution given the same context is acceptable as long as it is 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.

On the one hand, looking at Equation 3, by reducing the noise injection scale γ , our denoising process collapses to the deterministic probability flow (which is a gradient ascent of the noise target distribution) with noise decay. The smaller the γ , the more likely the generated sample lies close to the maximum of density of a dominant mode in the target action distribution. We favor exploitation over exploration.

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 0. 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 of the denoising process to find good modes.

Furthermore, robotic manipulation distribu- tions conditioned by observations are intrinsically low dimensional and simpler than image distributions: the image dataset CelebA has extrinsic dimension 2 16 , intrinsic dimension around 25 [15] 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 ExplorationExploitation tradeoff. First, it is likely that a well-chosen denoising time schedule ( t j ) j ∈{ 1 , /uni22EF ,δ } allows to reduce the number of needed steps. Starting the denoising process from t δ < T eliminates an uninformative step, the normal distribution from which x t δ 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 ϵ θ ( x t ; t, o ) .

Second, reduce the noise injection scale favoring exploitation, reducing the probability of denoising into tensor of larger values hence larger clipping frequency.

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Third, use a clipping-free denoising process. One may replace the DDPM scheduler with a DDIM scheduler. In Chi et al. [3], DDIM was used as way to do less denoising steps transposing the initial purpose of Song et 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 denoised samples. A more principled training and sampling that we denote by EDM was proposed by Karras et al. [12]. In principle, one could use an off-the-shelf DDPM Diffusion Policy model and use an EDM sampler. However, in order to obtain competitive results, not only we had to retrain an EDM policy from scratch but also had to tune training hyperparameters (see Experiments section 4). Unlike

DDPM, the EDM policies were very unreliable across horizons, going from solving an environement in horzion 24 to not even reaching 50% success rate as shown in figure.

The first two solutions above are easy to implement and yield significative improvement over baseline as the Experiments section 4 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-Exploitation 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 a polar opposite of Image generation: image generation is limited in 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 a genetic algorithm. The heuristics we choose for our genetic algorithm measures how OoD a given sample is.

Algorithm 1 Genetic Diffusion Policy

Require: Diffusion Policy noise model ϵ θ with schedule ( α t ) t ∈[ 0 ,T ] . Stochastic denoising rule x t j -1 = D ( x t j , j, ϵ θ ( x i t j , t j )) . OoD score φ ( x i t j , t j , ϵ θ ( x i t j , t j )) . Population size P . Survival number S . Denoising steps N .

1: Sample x i t N ∼ N( 0 , 1 ) for i ∈ { 1 , /uni22EF , P }

2: j ← N

3: while j ≠ 0 do

4: j ← j -1

5: Compute scores p i ∶= φ ( x i t j , t j , ϵ θ ( x i t j , t j ))

6: Select S element in { 1 , /uni22EF , P } with ( i 1 , /uni22EF , i S ) ∼ Multinomial ( S, p 1 , /uni22EF , p P )

8: end while

7: x i t j -1 ← D ( x i i % S t j , j, ϵ θ ( x i i % S t j , t j ))

9: Return x 0 0

In details, we generate a 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 is chosen to enhance our diffusion models by favoring in distribution samples. Two families of scores φ are considered:

where T is a temperature factor, f is a scaling function and ˆ x 0 ∶= x t - √ 1 -α t ϵ θ ( x t ,t ) √ α t . See Algorithm 1.

The clip-based score family φ clip ,f,T is clearly motivated by the discussion of section 2.2 both theoretically and empirically. Theoretically, clipping occurs when ˆ x 0 is OoD, hence the importance

of clipped coordinates is a measure of OoD. Empirically, if clipping is the issue, favoring less clipped samples should yield significant improvement in sample quality.

The stein-based score φ stein ,f,T has a double theoretical motivation. First, reducing the noise injection breaks the Langevin process, a stein-fitness emulates the noise injection of the Langevin process. Second, the noise estimator is a direct measure of OoD since a high noise means that the sample is far away from every modes of the target distribution.

4 Experiments

4.1 Experimental Setup

We evaluate our 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 1, and Robomimic [9] benchmarks. Each Adroit task involves controlling a 24-30 DoF robotic hand to accomplish a distinct goal:

Pen: Orient a pen to a target angle.

Relocate: Grasp a ball and place it at a goal position.

Hammer: Pick up a hammer and strike a 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 δ ∈ { 1 , 2 , . . . , 10 } ∪ { 20 , 30 , . . . , 100 } ,

Noise scaling factor γ ∈ { 0 . 0 , 0 . 1 , . . . , 1 . 0 } ,

Action horizon h A ∈ { 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 [18, 19], learning rate 10 -4 , weight decay 10 -6 , batch size 64, and 200 epochs. Shortcut models [7] were re-implemented in PyTorch 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. A 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 1, rather than tuning it to its most optimal state. We test populations p ∈ [ 4 , 8 , 16 , 32 ] , temperatures t ∈ [ 1 , 10 , 100 , 1000 ] , and noise scales γ ∈ [ 1 , 0 . 6 , 0 . 3 , 0 . 2 , 0 . 1 ] .

4.2 Results

Number of diffusion steps The correlation between the number of steps n and clipping is evident across all tasks: reducing the number of diffusion steps lowers the amount of clipping. This leads to similar or better scores with a greatly reduced inference cost. We observe a peak, prior to n = 4 , at which the clip is minimal, i.e. the performance is optimal, see figure 5(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 3, the extent to which the clipping can be mitigated via noise reduction is limited. This means that a part of the clipping is inherent to the deterministic part of the denoising process, and not only to the noise injection from the Langevin process. Still, the increase in score resulting from using lower noise scales is significant, see figure 5(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, we tradeoff conditioning complexity for distribution complexity. In longer horizons, the model has to learn a 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 is suboptimal, with a significant number of non trivial distributions to learn. This tendency was even more present with EDM, as showed in Figure 5(c)

Shortcut Models In this experiment, we evaluate our approach against the shortcut models. Shortcut models [7], as prominent means of increasing sampling speed, train a model with a conditioning on the number of steps that will be taken in total, which enables outputting quality samples in one step. However, this speedup comes at the price of performance degradation. As shown in Frans et al.[7], DP's success rate can be reduced by as much as 20% on tasks such as the robomimic transport[9] 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 1 demonstrate that although the shortcut 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 used-while requiring only two steps. Starting from a solid off-the-shelf model with approximately a 70% success rate, our method achieves a 100%

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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 n = 5 ).

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success rate along with a substantial speedup. Our experiments also showed that GDP suffers from using γ = 1 . 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 a single forward pass per step. The NFE cost is the wall-time of a single call of the model on the population; the Step cost is the wall-time of the denoising step after NFE: computation of formula 2, computation of fitness score and population management. See table 3.

5 Related Works

First, the Shortcut baseline maybe seen as a 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 a very active research thread mostly trying to improve the Stochastic Differential Equation (SDE) solver using in the denoising process [21, 12, 22] while other approaches try to leverage a parallel sampling [23]. This last method may be compared to ours as their averaging method may be interpreted as a cross-breeding step. More generally, swarm methods for solving Partial Differential Equations (PDE) is active research thread [24]. Via Fokker-Planck equations, the PDE and SDE view points are dual to one another.

Third, by leveraging a simple metaheuristic on sample population, our Genetic Denoising Process open the door for a broad family of metaheuristics [25]. Our stein fitness score may be related to PDE-constrained swarm optimization [26].

6 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 as they drastically increase memory costs which are already an issue for high resolution image datasets. The tweaks analysed in Section 2.4 are also not as viable for image generation : Song et al. [11] 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 it includes neither cross-breeding 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 a more diverse conditioning. As a result, little improvement may be achieved by improving the denoising process, the bottleneck is expected to be the conditioning of the noise model ϵ θ . 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 a bias toward its mean when the noise is reduced. Formal statements and proofs of these relationships are left for future work.

7 Conclusion

All in all our experiments demonstrate that using our proposed approach, we can improve performance as well as sampling rate of off-the-shelf-models. 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 shortcut 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 is well trained and the target distribution sophisticated enough.

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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.

Google Research. DDPM CelebA-HQ 256 × 256. https://huggingface.co/google/ ddpm-celebahq-256 , 2022. Hugging Face model repository, accessed 2025-05-16.

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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 google/ddpm-celebahq-256 pretrained pipeline [30]. We tweak the associated scheduler to use γ = 0 , and run inference while setting the number of inference steps to 50. Note that with the initial number of steps, the sample converges to a uniformly gray square.

B Intrinsic Distribution Manifold Dimension Estimation

We subscribe to the Manifold Hypothesis [31, 32] stating that data distributions are supported by a submanifold of R n . The intrinsic dimension of a dataset refers to the minimum dimension of such a manifolds supporting the whole dataset. Several notions of intrinsic dimension of a dataset as usually considered, the most common are based on Minkoswki or Hausdorff dimensions [33, 34, 35, 15]. Since our work focuses on diffusion models, we favor the method proposed by Stanczuk et al. [36]. This methods allows to estimate the intrinsic dimension learned distribution of a diffusion model by doing a PCA of different values of the noise model ϵ θ .

C Extra Experimental Results

C.1 Link between clipping and Score

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These figures show that minimizing the stein score (or the norm of the estimated noise) is the best of simple genetic algorithm heuristics. Using clipping statistics as a 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 videos are generated using a genetic algorithm using γ = 0 . 2 , with stein score as heuristic. All non gdp videos videos are generated using a vanilla policy using γ = 1 . For the 2-step diffusion, we use 80 as the maximum timestep and 20 as the minimum timestep.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\nOverall, 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.\n\nLIMITATIONS:\n### 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}