Title: Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking
Venue: NeurIPS 2025
Accepted: yes
Keywords: Robot Learning

Behavior Cloning

Diffusion Model

Guidance
Abstract: Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong success rates and generalization, its inherent stochasticity can result in erroneous action sampling, occasionally leading to unexpected task failures. Moreover, OL control suffers from delayed responses, which can degrade performance in noisy or dynamic environments. To address these limitations, we propose two novel techniques to enhance the consistency and reactivity of diffusion policies: (1) self-guidance, which improves action fidelity by leveraging past observations and implicitly promoting future-aware behavior; and (2) adaptive chunking, which selectively updates action sequences when the benefits of reactivity outweigh the need for temporal consistency. Extensive experiments show that our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks.

Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking

Abstract

Generative Behavior Cloning (GBC) is a simple yet effective framework for robot learning, particularly in multi-task settings. Recent GBC methods often employ diffusion policies with open-loop (OL) control, where actions are generated via a diffusion process and executed in multi-step chunks without replanning. While this approach has demonstrated strong success rates and generalization, its inherent stochasticity can result in erroneous action sampling, occasionally leading to unexpected task failures. Moreover, OL control suffers from delayed responses, which can degrade performance in noisy or dynamic environments. To address these limitations, we propose two novel techniques to enhance the consistency and reactivity of diffusion policies: (1) self-guidance, which improves action fidelity by leveraging past observations and implicitly promoting future-aware behavior; and (2) adaptive chunking, which selectively updates action sequences when the benefits of reactivity outweigh the need for temporal consistency. Extensive experiments show that our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks. Our code is available at https://github.com/junhyukso/SGAC .

1 Introduction

With the rapid advancement of generative models [1, 2, 3, 4] across a wide range of domains [5, 6, 7, 8], their adoption in robot learning is also accelerating [9, 10, 11, 12]. One compelling direction is Generative Behavior Cloning (GBC), which reinterprets the classic problem of Behavioral Cloning (BC) [13] using the modern generative models. In traditional BC, expert demonstrations, pairs of observed states and corresponding actions, are collected to train a model that maps observations to actions. GBC extends this idea by leveraging the strong generalization capabilities of state-of-the-art generative models to learn this mapping more effectively. Recent studies [14, 9, 10] show that GBC can handle complex sequential decision-making tasks across diverse environments using only supervised signals, greatly simplifies sample collection and training process without the need of intricate reinforcement learning.

Among recent trends, one particularly notable line of research is the Diffusion Policy model [9]. By adapting the score-based diffusion process originally developed for vision tasks, this approach enables sequential action generation through iterative refinement in a stochastic action space. This method has demonstrated significantly higher success rates compared to prior works [15, 16], representing a promising direction in BC. In particular, the integration of open-loop (OL) control, where a single observation is used to generate a sequence of future actions, combined with the powerful generalization capability of diffusion models, leads to improved temporal consistency, higher effective

A

DiffusionStepk

robotmove

cupmove(stochastic)

t-△t

t

t

St

1 +w

EB(A, s)

St-△t

E(A,S)

p(a|St-△t)

p(a|st)

psG(al-)

p(alst)

Psc(al -)

(a) Self-Guidance (SG)

(b) Effect of Self-Guidance

control frequencies, and ultimately smoother, more stable motions with substantially better overall performance.

However, this approach also comes with inherent limitations. Owing to the stochastic nature of diffusion-based sampling, there remains a non-trivial risk of generating erroneous actions that can result in task failure. In OL control, even a single poor action can unfold over multiple consecutive time steps, leading to a significant drop in performance. Additionally, OL control lacks the ability to respond promptly to unexpected disturbances, making it particularly fragile in noisy or dynamic environments. Closed-loop (CL) control, where actions are generated at each time step based on real-time observations, offers a more reactive alternative. However, it introduces a different challenge: the difficulty of maintaining temporal consistency. Because diffusion models sample stochastically at every step, CL control often suffers from jittery or unstable behavior, which can severely degrade performance. These limitations raise two critical questions: How can we increase the likelihood of sampling high-quality actions? And how can we achieve both reactivity and consistency while leveraging the strengths of diffusion policies? Addressing these questions is essential for unlocking the full potential of diffusion-based decision-making systems in real-world applications.

In this study, we address these two fundamental challenges in diffusion-based control. First, we introduce a novel form of self-guidance that incorporates negative score estimates, derived from prior observations, into the diffusion denoising process. While diffusion guidance has been extensively studied in image generation to improve sample quality [17, 18, 19], its application to behavioral cloning remains largely unexplored, primarily due to the difficulty of defining reward signals for imitation learning [14]. By leveraging information already embedded in the model's past decision, our method guides the model toward more informed, high-fidelity action modes and enables forwardlooking extrapolation, all without requiring additional fine-tuning, as shown in Fig. 1.

In addition to this, we introduce adaptive chunking, a control mechanism that updates action chunks only when the benefits of increased reactivity outweigh the need for temporal consistency. This strikes a dynamic balance between the responsiveness of closed-loop control and the stability of open-loop planning. By combining self-guidance with adaptive chunking, our method significantly improves the performance of standard Diffusion Policy and other baselines. Extensive evaluations across simulated and real-world robotic environments demonstrate that our approach outperforms Vanilla Diffusion Policy by 23.25% and the state-of-the-art BID by 12.27%, while reducing computational cost by a factor of 16.

2 Preliminary and Related Works

This paper explores methods to improve BC performance using diffusion policy [9]. We first introduce the fundamental principles behind diffusion models and GBC, and then provide a clear comparison between OL and CL control, emphasizing the strengths and limitations of each.

2.1 Diffusion Models

Diffusion Probabilistic Models (DPMs) [20] have emerged as a powerful generative framework, where data generation is modeled as a gradual denoising process starting from a pure Gaussian distribution. Instead of directly learning the data distribution, DPMs aim to learn the transition

from a noise prior p T ( x ) to the target data distribution p data ( x ) . This generative process was first formalized by Denoising Diffusion Probabilistic Models (DDPM) [4]. In DDPM, the forward process q is defined as a fixed Markov chain that incrementally corrupts the data by adding Gaussian noise with a variance schedule β t ∈ (0 , 1) over time steps t = 1 , . . . , T :

By leveraging the properties of Gaussian distributions, one can sample x t at any timestep t directly from the original data x 0 , without sampling all intermediate steps:

where α t = 1 -β t and ¯ α t = ∏ t i =1 α i denote the accumulated noise schedule. The reverse process, which learns to recover clean data from noisy observations, is also modeled as a Gaussian distribution. It is parameterized as:

Here, µ θ ( x t , t ) and Σ θ ( x t , t ) are typically predicted by a neural network. In many implementations, the variance Σ θ is fixed to a predefined schedule (e.g., ˜ β t I ), while the mean µ θ is derived from a noise prediction network ϵ θ ( x t , t ) , trained to estimate the noise ϵ from the noised input. This formulation allows the model to gradually denoise x t over time, ultimately recovering a clean data from the noise distribution. DDPM [4] further demonstrates that predicting the injected noise ϵ is equivalent to minimizing a reweighted variational lower bound of the data log-likelihood. This leads to a remarkably simple training objective:

This formulation trains the network ϵ θ to recover the original noise from a noisy sample, effectively teaching it to reverse the diffusion process. Building upon this, Song et al. [21] showed that the reverse diffusion process can also be interpreted as solving a Stochastic Differential Equation (SDE) or an equivalent Probability Flow Ordinary Differential Equation (PF-ODE) :

Here, the drift term involves the score function ∇ x log p t ( x ) , which is approximated by a neural network s θ ( x , t ) . When the generative task is conditional , for example, guided by class labels, text prompts, or environment states, the score network is trained to predict the conditional score , s θ ( x , t | c ) ≈ ∇ x log p t ( x | c ) . This allows the model to generate samples from a conditional distribution p ( x | c ) , enabling controllable generation tailored to various downstream tasks.

2.2 Diffusion Policy for Generative Behavior Cloning

With this understanding of diffusion-based generative modeling, we now explore how these principles can be applied to the domain of control through GBC. Let us consider a demonstration dataset D = { τ j } N j =1 , where each trajectory τ j = { ( s ( j ) t , a ( j ) t ) } T j -1 t =0 consists of a sequence of state-action pairs collected from human experts. In this work, we train diffusion policy model [9], aiming to learn an implicit policy distribution p θ ( a t | s t ) instead of a deterministic mapping from states to actions. This distributional approach enables the model to capture the diversity in plausible actions of expert behaviors.The training is performed by maximizing the log-likelihood of expert actions under the learned policy, using the following BC loss:

Specifically, at time step t , we denote the action chunk as A t = a t : t + H , where d a is the dimensionality of each action. The diffusion policy learns to model the distribution over such action chunks using the following training objective, which mirrors the denoising score matching loss of Eq.(4):

Here, A k t = √ ¯ α k A t + √ 1 -¯ α k · ϵ represents a noised version of the action chunk A t at diffusion step k , where the noise schedule follows standard DDPM notation: α k = 1 -β k , and ¯ α k = ∏ k i =1 α i . During inference, the model samples a full action chunk a t : t + H ∼ p ( a t : t + H | s t ) conditioned on the current state. The first h actions of this chunk are then executed without replanning. In this setting, H is referred to as the prediction horizon , while h is the action horizon . By learning to predict joint distribution over long-horizon action sequences, the diffusion policy inherently acquires implicit long-term capabilities.

2.3 Trade-off between Open Loop and Closed Loop Controls

We define CL control as the case where action horizon h = 1 , and OL control as the case where h = H/ 2 , typically H = 16 , h = 8 , following the diffusion policy convention [9]. OL control is inherently vulnerable to unexpected disturbances that may occur within its h -step execution window as the entire actions a t : t + H is generated based solely on the past state s t . (E.g; It's corresponding to 0.25s in 30Hz control frequency) In contrast, CL control replans at every step ( h = 1 ), allowing it to react immediately to sudden changes in the environment. However, this frequent regeneration often compromise long-term planning and disrupts consistency between consecutive actions. This limitations reflect an inherent trade-off between consistency and reactivity, which must be carefully balanced in control design.

Due to its importance, several studies have attempted to address this inherent trade-off. ACT Policy [22], for instance, proposes the use of Exponential Moving Average(EMA), which ensemble current and past predictions to enhance temporal consistency. Most recently, BID [23] proposes a testtime search strategy that samples multiple candidate actions and selects the optimal one using two criteria: (i) backward coherence , which prefers actions that are most consistent with the previously executed ones, and (ii) forward contrast , which favors candidates that differ significantly from those generated proposed by a separate 'negative' (i.e., undesirable) model. While BID yields respectable performance gains, it comes at the cost of significant computation and inference latency, due to the need to evaluate numerous candidates and maintain an auxiliary model during inference.

2.4 Limitations of Prior Score Guidance in Diffusion Control

While diffusion policies sample actions a t ∼ p θ ( a t | s t ) based on the current state s t , the inherent stochasticity of generative models introduces the risk of producing low-fidelity samples , that is, actions with low compatibility or likelihood under the given state. Fig. 2 (a) illustrates the distribution of action chunks generated by a Vanilla Diffusion Policy [9]. As shown in the Fig. 2, a non-negligible subset of samples exhibits ambiguous or intermediate behaviors. These low-fidelity actions lead to degraded task performance, as shown in Fig. 2 (c). This issue becomes even more critical in stochastic environments, where the agent must rapidly adapt to newly observed states s t .

A useful analogy comes from text-to-image generation, where outputs may deviate from the intended prompt. In such settings, users can simply discard unsatisfactory images and regenerate new ones. However, in sequential control tasks, this kind of post-hoc selection is often infeasible. A single erroneous action during rollout can lead to task failure , making fidelity essential for reliable control.

How, then, can we sharpen the distribution to filter out low-probability samples and enable rapid adaptation to changing states? A widely adopted approach in the image generation domain is Classifier-Free Guidance (CFG) [18], which modifies the denoising score during the diffusion process to steer the model toward more desirable outputs. Specifically, CFG applies the following guidance:

Here, w ∈ [0 , + ∞ ] is referred to as the guidance scale and ∅ denotes null (unconditional). Recall that the noise prediction ϵ θ in diffusion models is proportional to the score of the data distribution, i.e., ϵ θ ( x t , t, c ) ∝ ∇ x t log p t ( x t | c ) . Under this formulation, the modified score leads to a sampling distribution of the form p θ ( a | s t ) · ( p ( a | s t ) /p ( a )) w ∝ p θ ( a | s t ) · ( p ( s t | a )) w , where the original distribution is effectively reweighted by a reward signal-namely, the classifier probability p ( s t | a ) . Although this guidance mechanism has proven effective in the image generation domain [18], we observe that it does not translate well to sequential decision-making tasks, as demonstrated in Fig. 2(c), and similarly reported in prior work [14].

Vanilla

CFG

AG

SG(Ours)

1.0

0.873

0.753

0.8

SuccessRate

0.496

0.6

0.4

0.2

falsepositive

0.070

0.0

(a) w/o SG (Vanilla)

(b) with SG

(c)

Another alternative is AutoGuidance (AG) [19], which replace the unconditional output used in CFG with a conditioned output from an undertrained checkpoint , denoted as ϵ θ ′ ( x, s t ) . This method builds on the insight that CFG's score modification can be interpreted as an extrapolation away from the output of a negative or 'bad' model, thereby enhancing the desired 'good' distribution [19]. The modified score of AG is computed as:

As shown in Fig. 2 (c), AG significantly improves performance, highlighting the importance of filtering out false-positive actions. However, despite its effectiveness, AG has several limitations: (i) AG requires an additional checkpoint, doubling storage requirements; (ii) it relies on two separate model weights ( θ and θ ′ ), which requires computing both noise predictions in multiple inferences; (iii) the selection of the 'bad' checkpoint θ ′ introduces an additional hyperparameter.

3 Methods

Motivated by the trade-offs and overhead observed in prior approaches, we present two novel methods that simultaneously improve reactivity and consistency in GBC, without requiring extra training or architectural changes. These techniques are designed to be lightweight and plug-and-play, making them easy to integrate into existing frameworks while delivering significant performance gains.

3.1 Self Guidance: Improving Fidelity and Reactivity of Diffusion Policy

Departing from prior methods that rely on auxiliary models or handcrafted guidance signals, we introduce a self-guided mechanism that is simpler, more efficient, and surprisingly more effective. Rather than introducing external guidance sources as in previous work, we propose a novel selfreferential strategy that conditions on the model's own recent outputs-eliminating the need for extra models, tuning, or compute. Specifically, our Self Guidance (SG) is formulated as follows:

All that is required in SG is a single batched inference pass, using a concatenated conditioning input composed of the current and past states, [ s t , s t -∆ t ] . This simple design makes SG highly efficient in both implementation and runtime, which is especially advantageous in resource-constrained scenarios.

For a more comprehensive understanding of SG, we provide a deeper analysis of its sampling behavior. Similar to CFG, SG modifies the sampling distribution as follows:

This formulation encourages the model to assign higher probabilities to actions that deviate from those conditioned on the past state s t -∆ t , effectively guiding the model to adapt more rapidly to the newly observed state s t .

2.0

2.2

2.4

2.6

2.8

3.0

0.2

0.4

0.6

0.8

1.0

Push-T Performance

2.3 2.4

2.4

2.5

2.7

P=0

P=1

P=2

P=3

P=4

w

1.0

similarity

0.9

0.7

Cosine

0.6

Attemptto

Transporting

Attemptto

TaskComplete

0.4

grabthenut

placenutonrod

0.3

0

25

50

75

100

125

150

175

200

Timestep

Open-loop

Closed-loop

To give qualitative validation, we analyze how guidance strength affects overall performance under varying levels of stochasticity. In Fig. 3, the x-axis denotes the guidance weight w , while the y-axis shows the average final reward over 100 episodes. As the level of stochasticity increases, the optimal guidance weight rises accordingly-indicating that stronger guidance is beneficial under greater uncertainty. Notably, even in the absence of injected noise, SG significantly outperforms the vanilla setting ( w = 0 ), demonstrating its effectiveness.

To deepen our understanding of the SG mechanism, we present an additional theoretical perspective based on temporal extrapolation. Under this view, Eq. 10 can be rewritten as:

Assuming ∆ t is small and ϵ θ ( x, s ) is locally smooth and differentiable with respect to the state s , the term 2 · ϵ θ ( x, s t ) + ϵ θ ( x, s t -∆ t ) can be interpreted as a first-order approximation of ϵ θ ( x, s t +∆ t ) , with higher-order terms O ((∆ t ) 2 ) being negligible. With this interpretation, the guidance mechanism effectively encourages the model to sample from a modified distribution: p new ( a t ) ∝ p θ ( a t | s t ) 1 -w · p θ ( a t | s t +∆ t ) w , which represents a weighted blend between the current state s t and an extrapolated future state s t +∆ t . This allows the model to generate actions that implicitly anticipate short-term future dynamics, thereby improving its ability to adapt rapidly and respond proactively to changes or disturbances in the environment.

3.2 Adaptive Chunking : Improving Consistency while Reactive

In addition to SG focusing on improving reactivity through more adaptive sampling, we now turn our attention to another key challenge in sequential control: maintaining temporal consistency without sacrificing responsiveness.

Due to its stochastic nature, diffusion policy tends to be less compatible with CL control, often exhibiting issues such as jittering or idling. On the other hand, the main limitation of OL control is its lack of reactivity, which leads to significant performance degradation in noisy environments.

Importantly, the effectiveness of each control mode depends heavily on the characteristics of the target operation. For tasks that require delicate and precise actions, such as grasping an object, the acceptable action space is narrow, and motor deviations must be minimal. In such cases, the instability typically associated with CL control is negligible, while its reactivity provides a clear advantage in responding to external disturbances. Conversely, for tasks involving large-scale movements, such as transporting or lifting an object, the action space is broader, and step-by-step replanning in CL control can introduce unnecessary acceleration changes, often leading to task failure. In these scenarios, OL control is more stable and preferable. Therefore, both CL and OL control offer distinct advantages depending on the context, highlighting the need for action-aware adaptive control strategies.

Adaptive Chunking Based on this observation, we propose an adaptive chunking method that selectively maintains open-loop execution when consistency is high, and reverts to closed-loop

Vanila

EMA

BID

Ours

Push-T

Kitchen

Lift

Square

Can

Transport

Average

0.9

0.5

0.8

0.9

1.0

0.7

0.8

Success Rate

0.8

0.6

0.8

0.6

0.7

0.7

0.4

0.7

0.9

0.4

0.5

0.6

0.6

0.5

0.3

0.2

0.5

0.8

0.4

0.5

0.4

0.2

0.0

0.4

0.7

0.3

0.4

Push-T

Kitchen

Lift

Square

Can

Transport

Average

0.9

0.6

1.0

0.9

1.0

0.7

0.8

Success Rate

0.8

0.5

0.8

0.9

0.7

0.9

0.6

0.7

0.4

0.7

0.8

0.6

0.5

0.3

0.8

0.6

0.7

0.5

0.6

control when reactive updates are needed. Specifically, the model continues to use a previously planned action chunk as long as the similarity between the first action in the chunk and the newly generated action remains above a certain threshold.

Let A queue denote the action chunk queue, ˆ a t : t + H ∼ π ( a | s t ) the newly predicted action chunk, and τ the similarity threshold. The update rule is defined as:

where cos( · ) denotes cosine similarity. At each timestep, the first action in the queue is dequeued and executed: a t = A queue .dequeue() .

This adaptive strategy enables the controller to operate in a closed-loop fashion during high-precision phases and switch to open-loop execution when exact actions are less critical. As a result, it effectively mitigates compounding errors while avoiding the typical problems of closed-loop control such as jittering and idling. Fig. 4 illustrates the similarity between actions from a previously planned chunk and newly replanned actions, along with the corresponding control mode selected by our adaptive chunking scheme. By dynamically selecting the appropriate control mode based on the execution phase, our method achieves significantly higher success rates across a variety of scenarios.

4 Experiments

To validate the effectiveness of our proposed method, we conduct experiments across various tasks and environments, ranging from simulation benchmarks to real-world applications. Moreover, we perform extensive ablation studies to investigate the impact and performance contributions of the different components integrated into our approach.

4.1 Simulation Experiments

We first evaluate the performance of our method on behavioral cloning tasks within six simulation environments. These include simple tasks like PushT [9], standard benchmarks from Robomimic [24], and the particularly challenging long-horizon Kitchen [25] environment. Success Rate is used for

Top

wrist

front

a

(b) Initialize

(c)Pick

(d) Perturbation

(e) Place

main metric for most tasks, except for Push-T, which used target area coverage. For fair comparison, we endeavor to follow the evaluation setups of [23], with the primary modification being the use of a DDIM-30 [26] solver instead of the DDPM-100 [4] solver employed in the original work. Detailed setup configurations and results obtained using other solvers are included in the supplementary.

Baselines To demonstrate the effectiveness of our method, we conducted experiments comparing it not only against the Vanilla Diffusion Policy [9] but also against two other inference methods :

Exponential Moving Average (EMA) : Introduced in [22], which also called temporal ensembling. During inference, actions are mixed with the previous action using a ratio λ : ˆ A t = λ · A t -1 +(1 -λ ) · A t to enhance action smootheness. We set λ = 0 . 5 .

Bidirectional Decoding (BID) [23]: A state-of-the-art inference method for behavioral cloning that employs heavy test-time-search to select the optimal action sequence for a given state. We follow the default settings proposed in the original BID for fair comparison. Please refer to [23].

Problem Setup We consider two distinct problem setups as follows:

(i) Stochastic : Following [23], we introduce temporally correlated action noise during the manipulation task execution to simulate actuator noise or external disturbances. In this setting, closed-loop control is employed for fair comparison.

(ii) Static : We assume an ideal, clean environment without any external disturbances or noise. In this setting, open-loop control is utilized for all methods.

Results Fig. 5 illustrate the performance of our method compared to baselines in both stochastic (top) and static (bottom) cases. As shown, while EMA often improves performance, it results in performance degradation on some tasks. In contrast, both BID and our method consistently enhance performance across the evaluated simulation environments. However, BID not only worse than ours in performance but also it requires significant computational overhead - 16x more FLOPs and 2x slower latency. Our method, conversely, achieves superior performance than Vanilla DP by 23.25% and BID by 12.27%-without incurring additional computational cost.

4.2 Real World Experiments

We further validate the practical applicability of our method through real-world experiments. Specifically, We utilized a Lerobot(Huggingface) [27] implementation of Diffusion Policy(DP), and deployed it on SO-100 low cost robot arm [28]. We employ 3-camera setup, top (bird's-eye), front, and wrist views visualized in Fig. 7(a). All experiments are conducted on one A6000 GPU server

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

Rel. Perf. Gain

(Vanilla)

EMA

Push-T

Can

Transport

0.0

0.2

0.4

0.6

0.8

1.0

(Vanilla)

AC

with DDIM-10 Solver with 30Hz standard visuomotor control frequencies.

Problem Setup We design simple pick-and-place task using pen holder and cup. The task involved picking up a pen-holder grip and placing it into a cup, as shown in Fig. 7. Similar to Sec. 4.1, we evaluated performance under two conditions: (i) Stochastic : The target cup is moved during task execution to introduce environmental disturbance; (ii) Static : The target cup remained stationary.

Results In Fig. 9, we report the Success Rate of Vanilla DP[9] and Ours accross 20 trials. As shown in Fig. 9, our method demonstrated stronger performance than the vanilla , especially in dynamic environments. This confirms the effectiveness and robustness of our approach beyond simulation and

0.0

0.5

1.0

1.5

2.0

2.5

3.0

w

0.0

0.2

0.4

0.6

0.8

1.0

Push-T Performance

2.3 2.4

2.4

2.4

2.7

0.8

0.9

1.1

1.1

0.1

(Vanilla)

[SG] P=0

[AG] P=0

[SG] P=1

[AG] P=1

[SG] P=2

[AG] P=2

[SG] P=3

[AG] P=3

[SG] P=4

[AG] P=4

its applicability to real-world scenarios with noisy hardware and potential disturbances. Moreover, while BID [23] shows halting behavior

4.3 Ablation Studies

Different Levels of Stochasticity Table 1 presents the performance results under varying environmental noise scale ( P ) on PushT task. The detailed experimental setup is in appendix. As shown, the performance of baselines degrades rapidly as noise scale increases. In contrast, ours maintains performance and outperforms other methods, showcasing effectiveness of Self Guidance's reactivity enhancement and robustness of Adaptive Chunking.

Comparison with AutoGuidance [19]. To highlight the superior performance of our method, we conduct a detailed comparative study between our Self-Guidance (SG) and AutoGuidance (AG) [19]. In Fig. 15, we plot the performance of both methods across different guidance scales, ranging from w = 0 (no guidance) to w = 3 , in various environment noise levels ( P ). As shown, while both methods improve performance with guidance, our SG consistently achieves a higher peak performance than AG across all evaluated noise levels. Moreover, AG's performance degrades rapidly as the noise scale increases, whereas our SG maintains its robustness even in noisy environments. Finally, AG introduces a significant computational burden, including storage costs for the weak model's weights and an increased effective latency due to inability due to an inability to perform. In contrast, our SG incurs no computational overhead while delivering superior performance.

Sensitivity Analysis While EMA [22] can achieve good performance with an optimal decay rate, it is often overly sensitive. In Fig. 6, we present a parameter sensitivity analysis comparing EMA's decay rate λ with the threshold τ of our Adaptive Chunking (AC). As shown, EMA exhibits significantly different optimal decay rates across tasks. In contrast, our AC demonstrate consistent performance trends, highlighting their notable hyperparameter robustness and real-world applicability.

Individual effect of SG and AC In Fig. 10, we present an ablation study evaluating the impact of applying only Self Guidance (SG), only Adaptive Chunking (AC), and both components (Ours). As shown, the results indicate that while using either SG or AC alone improves performance over the baseline, the combination of both yields the best results.

5 Discussion : Extension to VLAs

While we mainly demonstrated our method with diffusion policy, our method can be extended to any behavior cloning framework that utilizes action chunking and probabilistic modeling of action space. To further validate the effectiveness and generality of our approach, we conducted experiments with two modern, state-of-the-art Vision-Language-Action models (VLAs) : π 0 and OpenVLA-OFT.

π 0 (pi-zero) [12] is a recently proposed, state-of-the-art VLA pretrained on web-scale data, which demonstrates the potential of leveraging the embedded world knowledge of foundation models for general-purpose robotic planning and control. Specifically, it employs an early-fusion approach to process multimodal inputs and directly generates chunked actions through a denoising process. We integrated our SG method directly into this denoising stage also with AC.

Stochastic

Static

0.5

0.6

0.7

0.8

0.9

1.0

Success Rate

Vanilla

Ours

0.2

0.4

0.6

0.8

Success Rate

Stochastic

Static

Vanila

SG

AC

SG + AC (Ours)

OpenVLA-OFT [29] is another web-scale, fine-tuned Vision-Language-Action (VLA) model designed for robotic tasks, which also utilizes action chunking(OL) for control. However, since OpenVLA-OFT is not diffusion-based, our original SG method cannot be directly applied. To address this, we introduce an variant of our SG, inspired by recent LLM guidance techniques, activation steering [30].

Specifically, During the forward computation of i -th Transformer blocks T i , we inject negative guidance using past activation A i t -1 , as follows :

This formulation in Eq. 15 is analogous to SG (Eq. 10) where we now applies guidance in feature-space instead of denoising output space in diffusion.

Table 2: Performance of OpenVLA on LIBERO with different noise scale P Experimental Results We use the LIBERO-Spatial benchmark [31] to evaluate performance. Similar to Sec. 4.2, we adopt a stochastic environment where target objects are in motion. Detailed experimental settings are provided in the Appendix. In Table 2, we compare the performance of original π 0 [12] and OpenVLA-OFT [29], which executed on open-loop control, and its closed-loop variant, finally with ours . As shown, the performance of vanilla VLAs with open-loop control decreases significantly in a stochastic environment (large P ). While the closed-loop version shows some improvement in high-stochasticity regions, this improvement is marginal. In contrast, π 0 and OpenVLA-OFT combined with our method achieves the best performance across all tasks, highlighting its broad applicability and potential for future extensions to VLA-style models.

6 Conclusion and Limitations

In this work, we demonstrate that Generative Behavior Cloning, particularly Diffusion Policy, can suffer from low-fidelity issues and a reactivity-consistency trade-off. To address these, we propose two novel techniques: Self-Guidance, which injects past score predictions as negative guidance, thereby enhancing fidelity and reactivity; and Adaptive Chunking, which dynamically balances reactivity and consistency. Our experimental results show that our approach consistently improves robotic control quality across diverse scenarios, including both simulation and real-world applications.

Limitations One limitation of adaptive chunking is its computational cost, which is comparable to that of CL control due to step-wise similarity evaluations. Nevertheless, we view this as a valuable opportunity for future work, and believe that designing more computationally efficient similarity measures could further enhance the practicality of adaptive chunking.

Acknowledgment

This work was supported by IITP and NRF grant funded by the Korea government(MSIT) (No. RS-2019-II191906, RS-2023-00213611, RS-2024-00457882).

References

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM , 63(11):139-144, 2020.

Lucas Pinheiro Cinelli, Matheus Araújo Marins, Eduardo Antúnio Barros da Silva, and Sérgio Lima Netto. Variational autoencoder. In Variational methods for machine learning with applications to deep networks , pages 111-149. Springer, 2021.

George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research , 22(57):1-64, 2021.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models, 2020.

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems , 33:1877-1901, 2020.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. Highresolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10684-10695, 2022.

Chengyi Wang, Sanyuan Chen, Yu Wu, Ziqiang Zhang, Long Zhou, Shujie Liu, Zhuo Chen, Yanqing Liu, Huaming Wang, Jinyu Li, et al. Neural codec language models are zero-shot text to speech synthesizers. arXiv preprint arXiv:2301.02111 , 2023.

Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al. De novo design of protein structure and function with rfdiffusion. Nature , 620(7976):1089-1100, 2023.

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 02783649241273668, 2023.

Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246 , 2024.

Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr00t n1: An open foundation model for generalist humanoid robots. arXiv preprint arXiv:2503.14734 , 2025.

Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. pi_0 : A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164 , 2024.

Dean A Pomerleau. Alvinn: An autonomous land vehicle in a neural network. Advances in neural information processing systems , 1, 1988.

Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell, Mingfei Sun, Raluca Georgescu, Sergio Valcarcel Macua, Shan Zheng Tan, Ida Momennejad, Katja Hofmann, et al. Imitating human behaviour with diffusion models. arXiv preprint arXiv:2301.10677 , 2023.

Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín. What matters in learning from offline human demonstrations for robot manipulation. arXiv preprint arXiv:2108.03298 , 2021.

Nur Muhammad Shafiullah, Zichen Cui, Ariuntuya Arty Altanzaya, and Lerrel Pinto. Behavior transformers: Cloning k modes with one stone. Advances in neural information processing systems , 35:22955-22968, 2022.

Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems , 34:8780-8794, 2021.

Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 , 2022.

Tero Karras, Miika Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine. Guiding a diffusion model with a bad version of itself. Advances in Neural Information Processing Systems , 37:52996-53021, 2024.

Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning , pages 2256-2265. pmlr, 2015.

Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems , 32, 2019.

Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprint arXiv:2304.13705 , 2023.

Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee, Maximilian Du, and Chelsea Finn. Bidirectional decoding: Improving action chunking via closed-loop resampling. arXiv preprint arXiv:2408.17355 , 2024.

Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, and Roberto Martín-Martín. What matters in learning from offline human demonstrations for robot manipulation. arXiv preprint arXiv:2108.03298 , 2021.

Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman. Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning. arXiv preprint arXiv:1910.11956 , 2019.

Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 , 2020.

Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/ diffusers , 2022.

Remi Cadene, Simon Alibert, Alexander Soare, Quentin Gallouedec, Adil Zouitine, and Thomas Wolf. Lerobot: State-of-the-art machine learning for real-world robotics in pytorch. https: //github.com/huggingface/lerobot , 2024.

Moo Jin Kim, Chelsea Finn, and Percy Liang. Fine-tuning vision-language-action models: Optimizing speed and success. arXiv preprint arXiv:2502.19645 , 2025.

Alessandro Stolfo, Vidhisha Balachandran, Safoora Yousefi, Eric Horvitz, and Besmira Nushi. Improving instruction-following in language models through activation steering. arXiv preprint arXiv:2410.12877 , 2024.

Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. Advances in Neural Information Processing Systems , 36:44776-44791, 2023.

Seyedmorteza Sadat, Manuel Kansy, Otmar Hilliges, and Romann M Weber. No training, no problem: Rethinking classifier-free guidance for diffusion models. arXiv preprint arXiv:2407.02687 , 2024.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. Conference on Computer Vision and Pattern Recognition (CVPR) , 2016.

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) , 2009.

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: The proposed problems of low fidelity and the consistency-reactivity challenge are addressed by our methods: self-guidance and adaptive chunking.

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in 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.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: We propose limitations of our methods in conclusion section.

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 in 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 on 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 a few datasets or with a 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, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a 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 be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [Yes]

Justification: he proposed theoretical result that Self-Guidance (SG) encourages sampling from a new, adjusted distribution is proven using the CFG formula, and a proof for future state extrapolation is also provided.

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 crossreferenced.

All assumptions should be clearly stated or referenced in 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 a short proof sketch to 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

Question: Does the paper fully disclose all the information needed to reproduce the main experimental 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)?

Answer: [Yes]

Justification: We have provided detailed experiment setup in main paper and appendix. Also, we provide our source-code to reproduce results.

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 a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to 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 to 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 a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to 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 be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: We append our source code in supplementary material to reproduce experimental results.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not 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 a 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: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on 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. If only a subset of experiments are reproducible, they 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) is 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, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We attached detailed hyperparameters in appendix.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a 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, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: We have included standard deviation error bars in most of our experimental results. These error bars were derived from 100 runs performed for each of 3 random seeds, totaling 300 runs.

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, confidence intervals, or statistical significance tests, at 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 run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to 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.

It is 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 a 96% CI, if the hypothesis of Normality of 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 are out of range (e.g. negative error 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

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: We attach our compute resources information in appendix.

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, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We have carefully reviewed the NeurIPS Code of Ethics and confirm that our research fully aligns with all its principles.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in 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]

Justification: This works focusing on improving performance of diffusion policy.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

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 a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an 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 to 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 as 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 to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA]

Justification: Our works does not have such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a 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.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: 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?

Answer: [Yes]

Justification: We have properly cited the codebase we implement our algorithm.

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 a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it 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: [Yes]

Justification: We provided detailed README in supplementary material.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of 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 is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing 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 as details about compensation (if any)?

Answer: [NA]

Justification: We have nor crowdsourcing or human subjects research.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS 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 an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: We have nor crowdsourcing or human subjects research.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

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

Question: Does the paper describe the usage of LLMs if it 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, or originality of the research, declaration is not required.

Answer: [NA]

Justification: We used LLM for just editing.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

A Experimental Details

Hyperparameter Settings The hyperparameters used in our simulation experiments in main paper are summarized in Table. 3.

Table 3: Additional hyperparameters for simulation experiments.

Implementation of Perturbation P In P noisy environment setting, , we implement the disturbance by moving the T-block in a fixed direction at a velocity of P , which is the same implementation used in BID [23]. The goal of this stochastic scenario is to approximate environmental disturbances, such as slipperiness or wind.

B Experimental Results with DDPM-100 Solver

Fig. 3(main) reports the success rates for six tasks evaluated under two environments ( Stochastic & Static ) using the DDIM-30 solver [26]. To ensure a fair comparison with prior works [9, 23], we also visualize the results using the DDPM-100 solver [4], keeping all other hyper-parameters unchanged. As shown in Fig. 11, our method outperforms Vanilla Diffusion Policy [9] by 19.63% and BID [23] by 7.58% on average across the six tasks in the stochastic setting. In the static environment, our method still achieves higher performance, surpassing Vanilla Diffusion Policy by 1.90% and BID by 1.23% .

Vanila

EMA

BID

Ours

Push-T

Kitchen

Lift

Square

Can

Transport

Average

1.0

1.0

1.0

0.9

1.0

0.6

0.9

SuccessRate

0.8

0.8

0.8

0.9

0.8

0.9

0.5

0.6

0.6

0.7

0.8

0.7

0.8

0.4

0.4

0.4

0.2

0.2

0.6

0.7

0.6

0.7

0.0

0.0

0.5

0.6

0.3

0.5

Push-T

Kitchen

Lift

Square

Can

Transport

Average

1.0

0.8

1.1

1.0

1.0

0.8

0.9

Success Rate

1.0

0.9

0.7

0.9

0.6

60

0.8

0.9

0.6

0.8

0.8

0.4

0.8

0.7

0.5

0.7

0.2

0.7

0.6

0.8

0.4

0.7

C Comparison with different EMA Rate λ

BID [23] reports that an Exponential Moving Average(EMA) [22] can perform well on several tasks, but also that the result is highly sensitive to the decay rate λ , with the optimal value differing by task. Motivated by this, we evaluated EMA over λ ∈ { 0 . 0 , 0 . 1 , . . . , 1 . 0 } for every task. Fig. 12 shows results for the Stochastic setting using representative values λ ∈ { 0 . 1 , 0 . 3 , 0 . 5 , 0 . 7 , 0 . 9 } , which include the empirically optimal value for each task. As shown Fig. 12, our method surpasses EMA on most benchmarks, highlighting both the challenge of choosing appropriate λ for EMA and the robustness of ours.

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Success Rate

Push-T

0.2

0.3

0.4

0.5

Kitchen

0.0

0.2

0.4

0.6

0.8

Lift

0.4

0.5

0.6

0.7

0.8

0.9

Square

0.7

0.8

0.9

1.0

Can

0.3

0.4

0.5

0.6

0.7

Transport

0.4

0.5

0.6

0.7

0.8

Average

Vanilla

EMA(0.1)

EMA(0.3)

EMA(0.5)

EMA(0.7)

EMA(0.9)

BID

Ours

D Comparison with different similarity Metric in Adaptive Chunking

To investigate effect of other similarity metrics in Adaptive Chunking(AC), we replaced cos( · ) with the L 1 and L 2 distances in AC. A threshold of τ = 0 . 1 performed best for norm-based metrics, while τ = 0 . 97 is used for the cosine-based method. As shown in Table 4, cosine similarity achieves the best performance across all tasks.

E Similarity visualization in other tasks

To verify the generality of our observation in Sec.3.2 of main, we also visualized the similarity of actions with Can task in Fig. 13.

Similar to [Fig.4 of main], the similarity decreases noticeably during precise actions in the Can task, such as grasping or placing the object into the target bin.

1.0

similarity

0.9

0.7

Cosine

0.6

Attemptto

Transporting

Attemptto

TaskComplete

0.4

grabthecan

placethetargetbin

0.3

0

25

50

75

100

125

150

175

200

Timestep

F Performance of Additional Guidance Methods

In addition to our Self-Guidance(SG), we evaluate two additional self guidance approaches for ablation.

Noised Observation (NO) Instead of utilizing past condition as negative guidance, we also try to utilized directly perturbed condition as a bad output. Specifically,

where s denotes scailing factor. We set s = 0 . 1 empirically.

Time-Step Guidance (TSG) [32] Recent work [18] introduce following Time-Step guidance. In this method, the bad output is computed by perturbed denoising timestep with same condition distribution. Specifically,

where ˜ t denotes perturbed timestep embedding ˜ t = t + s · t α and s, a are hyperparameters of TSG. We set s = 2 , α = 1 , following default configuration of TSG.

Results As shown in Fig. 14, 'NO' also shows worse performance than Vanilla . While TSG outperforms Vanilla slightly, its improvements is still marginal. Our SG shows remarkable performance improvement compared to other guidance methods.

0.2

0.4

0.6

0.8

1.0

Success Rate

0.496

0.070

0.375

0.516

0.735

0.873

Vanilla

CFG

NO

TSG

AG

SG (Ours)

0.0

G AutoGuidance vs. Self Guidance

To present a detailed comparison between Autoguidance (AG) and Self-Guidance (SG), we depict the performance of both methods at different guidance scales in Fig. 15. As shown, our SG clearly achieves higher optimal performance than AG across various noise scales. Moreover, while AG's optimal performance decreases rapidly as the noise scale increases, our SG maintains robust performance in noisy environments.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

w

0.0

0.2

0.4

0.6

0.8

1.0

Push-T Performance

2.3 2.4

2.4

2.4

2.7

0.8

0.9

1.1

1.1

0.1

(Vanilla)

[SG] P=0

[AG] P=0

[SG] P=1

[AG] P=1

[SG] P=2

[AG] P=2

[SG] P=3

[AG] P=3

[SG] P=4

[AG] P=4

H Real World Experiments Details

This section describes the experimental details of Sec.4.2(main). We have detailed the SO-100 robot arm [28] and camera setup, training details, evaluation details for the both inference method Vanilla DP [9] and Ours .

Experimental Setup We perform real world experiments with SO-100 robot arm [28] with three cameras. As shown in Fig. 16, each camera records bird-eye, front, wrist view of robot arms. The input shapes of images are 1920 x 1080 px videos with 30 fps, but we down-sample the image shapes to 224 x 224 px for training and inference. To ensure consistency with the training environment, we set the robot's operation to 30 FPS during inference.

Problem Setup As a simplified version of Robomimic 'Can' task [24], we consider a task that robot grasps a pen-holder grip and placing it into cup. Fig. 7(b)-(d)(main) illustrates the total sequence

(a) Front view

(c) Wrist view

of placing tasks. For stochastic scenario, we move the cup to introduce disturbance, while static scenario maintain the position of both pen-holder grip and cup.

Training Details We make 300 demonstration episodes with lerobot open source [28]. For each demonstration episodes, initial place of pen-grip holder and cup are randomly chosen while robot arm starts with same rest position. We follow the Diffusion Policy [9] training recipe with few modifications to fit in real world. We use ResNet-50 [33] as vision backbone with IMAGENET [34] pretrained weight, and cosine LR scheduler starts with linear warmup 500 steps. We use early stopped 240K steps checkpoint, which requires 27H with one NVIDIA RTX 6000 Ada Generation GPU and AMD Ryzen Threadripper PRO 7985WX CPU. Additional hyperparameter details are listed in Table. 5

Evaluation In Sec.4.2(main), we compare with two models, Vanilla DP [9], and Ours, using the guidance weight w as 0.1 for SG, and the similarity threshold τ as 0.99 for AC. In the static scenario, we set four types of starting points, and measure the success rate from five experimental runs at each point, total 20 episodes. In the stochastic scenario, we introduce disturbance by moving the cup by hand after the robot arm grasped the pen-grip holder. We consider each evaluation episode as failure if it exceeds 30 seconds time limit or drops pen-grip holder before placing it to cup.

I Additional Real World Experiments

We conduct additional experiments in real-world scenarios to compare the performance of our proposed method (Ours) with that of Vanilla DP [9], EMA [22], and BID [23] in stochastic scenario.

Problem Setup Similar to the previous real world experiment with the Vanilla DP [9], we perform a task that grasp a pen-holder grip and placing it in a cup. To introduce a highly stochastic scenario, the cup periodically moves in a circular path. Fig. 17 visualizes the successful and failed samples of the task.

Baselines We collected 300 demonstration episodes of placing the pen-holder grip while the penholder grip and cup are in static scenario. The detailed hyperparameters are same to those presented in Table. 5, which is the Sec.4.2(main) experiment's baseline, excluding the batch size and the number of training steps. We trained a new baseline for 320K steps with a batch size of 16, employing a cosine warmup scheduler. For a fair comparison, we configured the baseline methods, EMA and BID, similarly to the Sect.4.1(main) experiments. For EMA, we set its decay rate λ to 0.5. For BID, we

Grasp fail

Idling action

(b) Sample failure from BID

0.3

0.4

0.5

0.6

0.7

Success Rate

Vanilla

EMA

BID

Ours

adopted its original settings, and choose the strong policy as 320K steps and the weak policy as 240K steps.

Evaluation We evaluated each method based on 20 task executions, each initiated from the same position. Fig. 18 shows the success rates of tasks in real-world experiments. EMA shows a lower success rate than the Vanilla Diffusion Policy. Both BID and Ours achieved higher success rates compared to the Vanilla DP, but Ours shows a slightly higher success rate as it performed the penholder gripping action more precisely. The success rate of each method was consistent with the analysis from the Sec.4.1(main) simulation experiments. As shown in Fig. 17, BID failures frequently resulted from idling actions following a grasp failure, which can also be observed in Vanilla DP and EMA. However, our method experienced fewer grasp failures and exhibited no idling actions during evaluation. We found that BID inference ran at an average of 16 Hz for each action generation, so the robot operates unsmooth and halting manner. But, Ours generate actions an average of 29 Hz, and move smoothly.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents two novel methods for improving diffusion policies: Self-Guidance (SG), which guides the diffusion process by negating the action probabilities of previous timesteps, and Adaptive Chunking (AC), which dynamically selects the length of the action chunk to balance reactivity and consistency. The experiments conducted demonstrate significant performance improvements across a variety of simulation benchmarks and real-world robotic manipulation tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The proposed methods (SG and AC) are innovative, simple, and practically appealing, requiring minimal changes to existing diffusion policy frameworks.\n\n- The experiments are comprehensive, covering both simulation benchmarks and real-world scenarios, with clear and statistically significant performance improvements.\n\n- The paper is written in a clear and easy-to-follow way.\n\n**Weaknesses:**\n\n- The paper could provide a more thorough intuitive discussion about cases where SG might not be beneficial. For example, in slow-changing scenarios (e.g., a robot continuing to move toward the same target for some time), the SG method seems likely to reduce the probability of repeatedly sampling the optimal action. \n\n- Equations 12 and 13 contain an error. The approximation for first-order differences is not as mentioned, but $f(x+\\delta x) = 2f(x)-f(x-\\delta x)$. This also invalidates the discussion following the equation.\n\n- While the authors argue that adaptive chunking can mitigate issues due to unexpected changes in the environment, it is less clear how it prevents jittering arising from sampling multimodal trajectories. For instance, consider a scenario where a robot must pass an obstacle, which can be approached either from the left or from the right. If the current action chunk in the queue directs the robot to move left, but the next sampled action chunk directs it to move right, adaptive chunking would replace the current action. In subsequent steps, the sampled action could revert again to the left, causing continuous toggling between these trajectories rather than smooth behavior. Clarifying or experimentally addressing this concern would strengthen the paper. Clarifying or experimentally addressing this concern would strengthen the paper.\n\nQUESTIONS:\n-  In Figure 3, it is unclear from the main text what exactly \"different levels of noise\" refer to (diffusion policy sampling process, the environment observations, or dynamics?). Also, including a baseline result for SG at w=0 (vanilla) would significantly improve interpretability, clearly illustrating the gains specifically attributable to SG.\n\n- The authors use cosine similarity to measure action similarity. However, this metric is primarily suitable for position control tasks. It should be clarified that for tasks involving joint angles or velocity control, metrics like L1 or L2 norms might be more appropriate. \n\n- In the experimental section, it is stated that closed-loop control was used for stochastic environments. This choice seems contradictory since the key advantage of adaptive chunking is precisely to automatically decide on the execution length. Either this choice needs clarification, or the experiment should be restructured to genuinely test AC.\n\n- Regarding the VLA experiment in the discussion, can something like that be done for ACT? I'm aware that such an experiment is out of the scope of a rebuttal, just interested in your thoughts on the matter.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents two training-free inference modules, Self-Guidance and Adaptive Chunking, to tackle low-fidelity action sampling and balance reactivity with temporal consistency in diffusion-based behavior cloning. Self-Guidance sharpens the action distribution by using the model’s past state predictions as negative guidance during denoising, thereby biasing sampling toward higher-fidelity and forward-looking actions without extra training. Adaptive Chunking dynamically alternates between open-loop and closed-loop control based on cosine similarity of successive actions, preserving consistency in bulk motions while enabling rapid replanning during precision phases. This work yields substantial performance gains over vanilla Diffusion Policy and state-of-the-art BID method in both simulation and real-robot tests.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\nThe proposed methods are plug-and-play and computationally efficient, requiring no retraining or architectural changes, yet consistently improve action fidelity and responsiveness across diverse manipulation tasks.\n\n**Weakness**\n\n1. The contribution seems limited: While the methods improve existing diffusion policies, they mainly adapt known ideas from classifier-free and auto-guidance. The novelty over AutoGuidance [1] and prior chunking strategies (i.e., BID) is incremental and could be more sharply distinguished.\n\n2. The extension to VLA-based models (OpenVLA-OFT) is still at an early stage and is not benchmarked against recent diffusion-head VLA architectures such as $\\pi _0$ or other $\\pi _0$-style methods (like UniVLA), leaving open the question of these techniques' performance on state-of-the-art policies. I would expect more real-world experiements on extension to other VLAs.\n\n3. The real-world robot is a little toy, but it's understandable.\n\n**References**  \n[1] Karras, Tero, et al. \"Guiding a diffusion model with a bad version of itself.\" Advances in Neural Information Processing Systems 37 (2024): 52996-53021.\n\nQUESTIONS:\n1. Could you clarify and empirically demonstrate your contributions different from AutoGuidance and BID? For example, are there task settings where those baselines fail but your methods succeed?\n2. Please extend OpenVLA-OFT evaluations to include direct comparisons with diffusion-head VLA architectures such as $\\pi_0$, UniVLA, or similar methods on standard benchmarks (e.g., LIBERO). This will show whether your inference modules work for state-of-the-art policies instend of pure diffusion policies only.\n3. I would expect more real-world experiements on extension to other VLAs.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-29"}

comments: SUMMARY:
This paper proposes an improved generative behavior cloning (GBC) framework, specifically targeting the application of diffusion strategies in robot learning. The authors point out two main problems of current diffusion strategies in open - loop (OL) control: (1) Randomness in the diffusion process may lead to incorrect action sampling; (2) OL control lacks the ability to quickly respond to dynamic environments. To address these issues, the paper proposes two innovative methods: Self - Guidance (SG) and Adaptive Chunking (AC).

STRENGTHS AND WEAKNESSES:
Strengths:
1. Proposes two innovative methods: Self - Guidance (SG) and Adaptive Chunking (AC).
2. Adapts SG to OpenVLA , showing applicability beyond diffusion models.
3.  Empirical validation with real-world experiments.
Weaknesses:
1. AC’s step-wise similarity evaluations incur latency comparable to closed-loop control.
2. Experimental design exhibits ​limited breadth in baseline comparisons. Boader comparisons would better contextualize its advancements.
3. SG’s extrapolation (Eq. 13) assumes local smoothness of ϵ, which may not hold for highly non-linear dynamics.

QUESTIONS:
1. The self-guidance (SG) formula (Eq. 10) uses past states $ s_{t-\Delta t} $ as negative guidance.  
  - How was the time window $ \Delta t $ optimized? Was it task-specific or theoretically derived?  
  - Were ablation studies conducted to compare SG with explicit future-state prediction?  
2. Why not use probabilistic metrics (e.g., KL divergence) for uncertainty-aware transitions?  
3. Does fixed-threshold chunking fail in edge cases (e.g., sudden disturbances)?
4. Would SG’s reactivity degrade with deformable objects or non-stationary tasks?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors investigate means of improving generative behaviour cloning policies, particularly the diffusion policy approach. They propose that one issue with these models is that they do not function as “closed-loop” policies, in which decisions are made at each state to complete a task. To address this limitation, they propose a novel self-guidance technique using subsequent states to modify the noising process during action generation. They then propose an adaptive mechanism to decide when the current action sequence differs too much from the model's output and to either continue executing the current plan or use the new action sequence. Experimentally, their approach improves control performance over baseline methods that propose alternative means of adapting action sequence generations with diffusion policies, showing strong performance improvements.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n-  Claimed contributions are clearly stated in the introduction, making it easier to understand the objectives of the paper\n- The proposed techniques are accessible and appear to require minimal changes to utilize Diffusion policies more effectively during deployment. \n- Experiments demonstrate both real-world results and simulation outcomes. The authors conduct ablation experiments to put the mechanisms proposed in the paper into context and assess their effectiveness and utility. Inclusion of sensitivity results \n\nWeaknesses:\n\nIn terms of the method techniques proposed and experiments conducted, the only two major concerns we had were about the following:\n- No experiments were done to help motivate determine the theoretical conclusions shown in Equations 12 - 13. This intuition of “predicting into the future” seems testable where instead of using the subsequent steps, the authors could use varying distal states to calculate the $\\epislon_{new}$, e.g. instead of $s_{t - \\delta t}$ use something like $s_{t - k \\delta t}$ where $k$ is varied over. \n- Experiments considering the tradeoffs in computation by using a “closed-loop” approach. The authors briefly mention these limitations, noting that their approach may be slower if queried more frequently. If the authors performed experiments to demonstrate this tradeoff in computation time to previous approaches, it would put the consequences of their techniques' tradeoffs into perspective between performance and computation, particularly for real-world robotic use cases. \n\nOther concerns we had which are more about how to position the work:\n- We did not find the motivations for closed-loop vs. open-loop compelling justifications for the work. The horizons the author mentions (H = 16 steps) seem short, particularly if there’s a high sampling rate on a robot. If your system works at 1000 Hz, what is the difference in precision you’re losing at such a short window of prediction? As the authors point out in their limitations (line 334) regarding computation costs, the bigger issue, in our opinion, is not being able to query the model fast enough for real-time control in a closed loop system. To our understanding, diffusion is a fundamentally iterative process, so generating a single action chunk could take more time than possibly hope to query in between decisions.  We suggest that the authors focus their paper more on discussing the improvement of diffusion policy inference than on making claims about the benefits of being  a closed vs. open-loop control system, becuase we do not agree that their results support this argument well. \n\n- The work should acknowledge fields addressing similar problems more beyond the diffusion policy research literature. In our opinion, the “adaptive action chunking” mechanism is similar in motivation to the stopping function defined in the options framework [1]. Likewise, fields like control theory have also studied the value of replanning action sequences despite being able to roll out multiple steps (e.g., model predictive control predicts multiple time-steps but typically only sends one command). It’s surprising, for instance, that lines 38-45 provide  no supporting research on the tradeoffs between “open loop” and “closed loop\" approaches. We would appreciate the authors acknowledging that these motivations of their work are also present in other domains. In our opinion, these ideas (OL vs CL, stopping functions) are not novel by themselves, but instead addressing these issue specifically in the context of diffusion policies is the real focus. \n\n[1] Sutton, Richard S., Doina Precup, and Satinder Singh. \"Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.\" Artificial intelligence 112.1-2 (1999): 181-211.\n\nQUESTIONS:\n- Line 119: Why is “h = H / 2”? \n- How fast do we need to be able to make a decision? Even in robotics, where sampling rates could make some difference \n- What is “P” in Figure 3 precisely? It says it’s noise levels, but is that referring to the standard deviation of the noise process or something else?\n\nLIMITATIONS:\nWe felt the author's work could benefit from a more extensive limitations discussion. Computation time for their approach seems a much more important issue than they allocate space for.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}