Title: Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
Venue: NeurIPS 2025
Accepted: yes
Keywords: stochastic interpolants

test-time compute scaling

generative robot control

diffusion policy

flow matching

robotic manipulation

difficulty classification

adaptive computation
Abstract: Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation-learning tasks. However, these controllers employ a fixed inference budget at every control step, regardless of task complexity, leading to computational inefficiency for simple subtasks while potentially underperforming on challenging ones. To address these issues, we introduce Difficulty-Aware Stochastic Interpolant Policy (DA-SIP), a framework that enables robotic controllers to adaptively adjust their integration horizon in real-time based on task difficulty. Our approach employs a difficulty classifier that analyzes RGB-D observations to dynamically select the step budget, the optimal solver variant, and ODE/SDE integration at each control cycle. DA-SIP builds upon the stochastic interpolant formulation to provide a unified framework that unlocks diverse training and inference configurations for diffusion- and flow-based policies. Through comprehensive benchmarks across diverse manipulation tasks, DA-SIP achieves 2.6-4.4× reduction in total computation time while maintaining task-success rates comparable to fixed maximum-computation baselines. By implementing adaptive computation within this framework, DA-SIP transforms generative robot controllers into efficient, task-aware systems that intelligently allocate inference resources where they provide the greatest benefit.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\n**Summary**  \nThis paper proposes **DA-SIP (Difficulty-Aware Stochastic Interpolant Policy)**, a novel framework for adaptive test-time computation in diffusion- and flow-based robotic controllers. The authors identify a key inefficiency in existing approaches: they use a fixed inference budget for all control steps, regardless of the underlying task complexity. To address this, DA-SIP introduces a **difficulty classifier** that dynamically assesses the complexity of a subtask from RGB-D inputs and selects the appropriate solver configuration, integration horizon, and ODE/SDE mode on the fly.\n\nThe method is grounded in the **stochastic interpolant (SI)** framework, which unifies diffusion and flow-based models and allows flexible inference configuration without retraining. \nThrough comprehensive benchmarking on robotic manipulation tasks, DA-SIP demonstrates **2.6–4.4× reductions in computation time** while maintaining **task success rates comparable to fixed-budget baselines**. The approach provides an efficient and scalable path forward for resource-aware generative robot policies.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths and Weaknesses**\n\n**Quality**  \n**Strengths:**  \n- The paper presents a well-motivated and clearly articulated problem: fixed inference budgets in flow- and diffusion-based policies result in inefficiencies across tasks of varying complexity.  \n- The proposed solution, DA-SIP, is technically sound and thoughtfully designed, leveraging the stochastic interpolant framework to enable adaptive inference configurations.  \n- Experiments are comprehensive and demonstrate strong empirical results: DA-SIP achieves a 2.6–4.4× reduction in computation without sacrificing performance.  \n\n**Weaknesses:**  \n- The method has only been evaluated in simulation. Real-world robot validation, though understandably out of scope, would further solidify the impact.  \n- The robustness of the method under noisy sensor inputs or misclassified difficulty levels is not fully explored.\n\n**Clarity**  \n**Strengths:**  \n- The paper is well-organized and clearly written, with good use of figures and examples to illustrate key components.  \n- Technical exposition is rigorous yet accessible, and contributions are clearly delineated.\n\n**Significance**  \n**Strengths:**  \n- This work addresses a highly practical and underexplored issue in generative robotic control: test-time computation efficiency.  \n- The proposed approach could inspire future research on resource-adaptive policies across other generative architectures and tasks.\n\n**Weaknesses:**  \n- While the method is promising, the lack of real-world deployment raises questions about generalizability beyond the simulation benchmarks.\n\n**Originality**  \n**Strengths:**  \n- The integration of adaptive compute via difficulty classification in a flow/diffusion-based framework is novel.  \n- The unification under the stochastic interpolant framework is conceptually elegant and technically innovative.\n\n**Weaknesses:**  \n- Some components (e.g., zero-shot and few-shot VLMs for difficulty classification) are based on prior ideas, although their integration into this specific context is novel.\n\nQUESTIONS:\n**Questions and Suggestions**\n\n1. **Difficulty Classification Accuracy and Impact**  \n   - How sensitive is the overall system performance to errors in the difficulty classification stage?  \n   - Have you conducted experiments where the classifier is intentionally degraded (e.g., with noise or randomization) to test robustness?  \n   - Clarifying this would help evaluate how reliable DA-SIP is under real-world noise or partial observability.\n\n2. **Computational Overhead of Difficulty Classifiers**  \n   - What is the runtime overhead of the different classifiers (lightweight CNN, zero-shot VLM, fine-tuned VLM)?  \n   - In high-frequency control scenarios, even lightweight models could become bottlenecks. Providing profiling data would help assess real-time applicability.\n\n3. **Transfer to Real-World Settings**  \n   - Although the paper mentions future plans to transfer to real robots, have the authors considered sim-to-real strategies or domains where DA-SIP could already be viable (e.g., industrial pick-and-place)?  \n   - Any partial evaluation on physical hardware would strengthen the practical impact.\n\nLIMITATIONS:\n**Limitations**\n\nThe authors have partially addressed the limitations of their work by acknowledging that their approach has only been evaluated in simulation and may not directly transfer to real-world robots without further adaptation. This is a fair and important caveat, given the challenges of sim-to-real transfer in robotic control.\n\nHowever, the paper does not explicitly discuss the **potential societal impacts** of adaptive compute in autonomous systems. While the work is primarily technical, the deployment of adaptive inference in robotics could have broader implications—for example:\n\n- **Resource allocation bias**: Adaptive inference could inadvertently prioritize certain tasks over others based on incorrect difficulty estimation, which might be problematic in safety-critical applications.\n\n**Suggestion**:  \nIt would strengthen the paper if the authors briefly commented on these broader considerations, particularly regarding deployment in human-centric environments or industrial systems where incorrect difficulty assessments could cause harm or inefficiency.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents Difficulty-Aware Stochastic Interpolant Policy (DA-SIP), which adatively adjusts the inference budget based on predicted task difficulty. The method is built upon the stochastic interpolant policy (SIP), which unifies diffusion-based and flow-based generative models. This enables the method to incorporate a difficulty classifier that estimates the task difficulty to select the appropriate step budget, the optimal solver variant, and ODE/SDE integration at each control cycle to balance the accuracy and efficiency. Across a suite of robotic manipulation tasks, the proposed method can achieve comparable success rates to fixed-budget baselines with 2.6-4.4× reduction in total computation time.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed method to unify diffusion and flow policies under stochastic interpolants introduces the possibility to switch between different configurations of generative models during training and inference.\n2. The method decreases the total computation time by 2.6-4.4× while maintaining comparable success rates to fixed-budget baselines.\n3. The difficulty model and the policy model architecture can be easily upgraded to more complex tasks without fundamental design changes.\n\n\nWeaknesses:\n1. The difficulty categories are too task-specific and may not be directly extended to other more complicated tasks, e.g., bimanual tasks, fine-grained in-hand manipulation tasks, articulated or deformable object manipulation tasks, etc.\n2. The use of manually labeled data for learning the difficulty model is not scalable. Considering the limitation of the manually defined difficulty categories, the dataset would have to be re-labeled whenever the inputs to the difficulty model are changed or the difficulty categories have to be re-defined.\n\nQUESTIONS:\n1. In equation 3, could you explain in detail why $s(\\mathbf{x}, t, \\mathbf{o})=\\sigma_t^{-1} \\mathbb{E}\\left[\\mathbf{\\varepsilon} \\mid \\mathbf{x}_t=\\mathbf{x}, \\mathbf{o}\\right]$?\n\nLIMITATIONS:\n1. The difficulty categories are too task-specific and may not be directly extended to other more complicated tasks, e.g., bimanual tasks, fine-grained in-hand manipulation tasks, articulated or deformable object manipulation tasks, etc.\n2. The use of manually labeled data for learning the difficulty model is not scalable. Considering the limitation of the manually defined difficulty categories, the dataset would have to be re-labeled whenever the inputs to the difficulty model are changed or the difficulty categories have to be re-defined.\n3. The method has yet to be tested on real robots and more complex tasks.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a test-time scaling approach, Difficulty-Aware Stochastic Interpolant Policy (DA-SIP), which adaptively chooses the interpolation configuration during inference. Specifically, DA-SIP first trains a difficulty classifier to assign a difficulty score for each state per step, and then selects a predefined inference configuration based on the score. The authors conduct experiments on diverse manipulation tasks to show that DA-SIP can significantly reduce the total computation time while maintaining the performance as maximum-computation baselines, showing the potential of efficient policy deployment.\n\nSTRENGTHS AND WEAKNESSES:\n1. Originality: Test-time scaling is one important research area for large models. While various works have explored test-time scaling methods in LLMs, their application in robotic manipulation with diffusion- and flow-based policies is, to the best of my knowledge, less explored. As the authors remark, the SI-grounded flow framework is one of their contributions. I'm wondering if this is a direct application of stochastic interpolant on robotic policy learning or if there are any major differences compared to the original work.\n2. Quality: Applying stochastic interpolant on test-time scaling for diffusion- and flow-based policies seems a natural choice. The experiments clearly show that the proposed DA-SIP can achieve lower latency than max-compute baselines. However, I have the following questions:\n   1. Could authors justify why max-compute is a good baseline to compare with? As the author mentioned in Section 4.2, different tasks require fundamentally different configurations to achieve optimal performance, so max-compute does not seem a perfect choice in general. Moreover, Table 4 and Table 5 illustrate that max-compute achieves very similar performance as min-compute in many tasks (Tool Hang, Square, Lift, Can) but needs substantially more time. In these tasks, DA-SIP seems to use a much longer time than the min-compute baseline.\n   2. How is time measured? Is this the wall-time or estimated by the compute-cost model defined in Section 3.5? Is the overhead of the classifier included?\n3. Clarity: The paper is well organized and clearly written, with a sufficient introduction of the background.\n4. Significance:\n   1. The idea of using a classifier to adaptively adjust the inference configuration is reasonable. However, the current implementation does not seem scalable. It seems that both difficulty categories and their corresponding inference triplets are manually designed with some heuristics, and the training data is also labelled by humans. Therefore, it might be hard to extend to broader scenarios where more categories need to be specified and each category requires a grid search over the best inference triplets. I'm wondering if there is a scalable way to do this, such as clustering categories with some unsupervised methods.\n   2. One merit of test-time scaling in LLMs is that the performance keeps increasing when we scale up the inference budget. While the paper presents some impressive results on how DA-SIP can be used to save computation, I'm also curious about whether such a trend exists for robotics manipulation. In Table 1, it seems that Tool Hang and Multimodal Ant are not using the maximum budget to achieve the optimal result. Does this imply that increasing the computation budget cannot further boost the performance?\n\nOverall, while the work has made some interesting exploration in test-time scaling for robotic manipulation, some parts of its results need further clarification. Given the number of open questions, I am unable to recommend acceptance at this stage.\n\nQUESTIONS:\nThere are some questions and concerns, which I have outlined in the previous section.\n\nLIMITATIONS:\nThe authors briefly discuss the limitation in the conclusion. In addition, some questions listed in *Strength And Weaknesses* may need further discussion.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis research introduces the Difficulty-Aware Stochastic Interpolant Policy (DA-SIP), a novel framework designed to improve the computational efficiency of robot controllers. Current state-of-the-art policies, based on diffusion and flow models, use a fixed amount of computational power for every action, regardless of the task's complexity. This can be inefficient, as simple movements receive the same resources as complex, precise maneuvers. DA-SIP addresses this by dynamically adjusting its computational budget in real-time. It uses a difficulty classifier, which can be a lightweight CNN or a Vision-Language Model (VLM), to analyze the robot's current situation from sensor data and determine how challenging the next action is.\n\nBy integrating this difficulty assessment with a flexible stochastic interpolant framework, DA-SIP can select the most appropriate solver, integration method, and step count for the immediate task. For instance, an easy motion like approaching an object might use a single computational step, while a delicate grasping action would be allocated a much higher budget to ensure success. This adaptive approach allows the system to conserve resources during simple phases and deploy them intelligently for more demanding parts of a task.\n\nThe authors validated DA-SIP across a variety of simulated robotic manipulation tasks, from simple lifting to complex placements. The results demonstrate significant efficiency gains, with the system reducing total computation time by a factor of 2.6 to 4.4 compared to traditional methods that always operate at maximum computational load. Crucially, this optimization was achieved while maintaining comparable or even slightly improved task success rates. The study concludes that adaptive computation is a highly effective strategy for making generative robot controllers more efficient and practical for real-world applications.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1.  **Novel and Relevant Problem Formulation:** The paper addresses a significant and practical problem in modern robotics: the computational inefficiency of generative policies. By framing the solution as dynamic, test-time compute scaling, it draws a creative and powerful parallel to adaptive computation in large language models and applies it effectively to the domain of robot control.\n\n2.  **Strong Theoretical Grounding:** The approach is built upon the Stochastic Interpolant (SI) framework, which unifies diffusion and flow-based models. This provides a principled and flexible foundation for the method, allowing it to manipulate various parameters like solver type, integration mode (ODE/SDE), and step count without retraining the core policy. This is far more elegant and robust than an ad-hoc or purely empirical solution.\n\n3.  **Comprehensive Experimental Validation:** The authors test their DA-SIP framework across a diverse suite of simulated manipulation tasks with varying characteristics (simple, precision, exploratory, and transport). This rigorous evaluation demonstrates the versatility of the approach and provides strong evidence that the benefits are not limited to a single, narrow task type.\n\n4.  **Thorough Exploration of Classifier Methods:** The paper thoughtfully evaluates three different methods for difficulty classification: a data-efficient lightweight CNN, a flexible zero-shot Vision-Language Model (VLM), and a balanced fine-tuned VLM. This analysis provides valuable insights into the trade-offs between performance, data dependency, and ease of deployment, which is crucial for real-world applicability.\n\n5.  **Clear and Impactful Results:** The research delivers impressive quantitative results, demonstrating a 2.6-4.4x reduction in computation time while maintaining, and in some cases even improving, the task-success rates of fixed, maximum-compute baselines. It also uncovers the counter-intuitive insight that excessive computation can sometimes degrade performance, adding a valuable new dimension to the understanding of these models.\n\n### Weaknesses\n\n1.  **Exclusive Reliance on Simulation:** The primary weakness is that all experiments are conducted in simulation. There is no validation on a physical robot, which leaves the critical \"sim-to-real\" gap unaddressed. Real-world challenges like sensor noise, unexpected physical interactions, and system latencies could significantly impact the performance and efficiency of both the difficulty classifier and the policy itself.\n\n2.  **Manual Design and Generalization Issues:** The framework's core logic relies on manually crafted components that may not scale or generalize easily. The six difficulty categories are task-specific, and the lookup table that maps these categories to computational settings appears to be hand-tuned. Adapting the system to new, unseen tasks would likely require a significant manual effort of re-defining categories and re-tuning this crucial table.\n\n3.  **Data Annotation Bottleneck:** The best-performing and most reliable difficulty classifier presented is a supervised CNN, which required a substantial dataset of 20,000 human-annotated states. This reliance on large-scale, manual annotation is a major practical bottleneck that hinders the framework's scalability and ease of adoption in new environments.\n\n4.  **Lack of Explicit Safety Guarantees:** The system's behavior is contingent on the accuracy of the difficulty classifier. An incorrect classification could cause the robot to use insufficient computation during a delicate maneuver, leading to failure or unsafe actions. The paper does not discuss or implement any safety layers or fallback mechanisms to mitigate the risks of such classification errors, which is a critical concern for physical deployment.\n\nQUESTIONS:\n1.  **Regarding the \"Last Step\" Method in Table 1:** In Table 1, the \"Last step\" for the *PushT* and *Block Push* tasks is listed as \"Tweedie\". Could you elaborate on what the Tweedie step refers to in this context and explain its function and importance for these specific precision manipulation tasks?\n2.  **On the Cost Function Formulation:** The compute-cost model is defined as $C(s_{t})=k_{t}\\gamma_{solv\\mathfrak{er_{t}}+c_{0}$, which accounts for the number of inference steps ($k_t$) and the solver type (Euler vs. Heun). However, the choice between ODE and SDE integration, which is a key part of the adaptive inference triplet, does not appear to be factored into this cost model. How is the computational cost of selecting an SDE over an ODE accounted for, or is its impact considered negligible?\n3.  **Design Rationale for the Lookup Module:** The mapping of difficulty categories to specific inference triplets in Table 3 (e.g., 'Stochastic attempts' mapping to 50 steps, Euler, SDE) is critical to the success of the DA-SIP framework. What was the methodology or heuristic used to design this lookup table? Was this mapping determined through systematic experimentation, or was it based on domain knowledge about the tasks and solvers?\n4.  **On Performance Degradation in Exploratory Tasks:** The paper notes that for exploratory tasks like *Tool Hang*, increasing computation from 50 to 100 steps surprisingly *decreases* performance. The \"threading a needle\" analogy is provided as a potential explanation. Could you provide a more detailed, data-driven explanation for this phenomenon? For instance, does excessive computation reduce beneficial stochasticity or cause the policy to overfit to specific, suboptimal trajectories?\n\n5.  **Investigating Classifier-Policy Mismatch:** The difficulty classifier is trained on human-annotated labels of \"difficulty,\" while the policy learns from robot trajectories. Is there a risk of a mismatch between what a *human perceives* as difficult versus what is *computationally challenging* for the policy? How sensitive is the overall task performance to classification errors (e.g., the 18% performance drop in the Transport task with the Zero-shot VLM)?\n6.  **Generalizability of Difficulty Categories:** The framework relies on six predefined difficulty categories (Initial, Near, Grabbing, etc.) derived from the evaluated tasks. How would this approach generalize to entirely new robotic tasks that may not fit neatly into this existing categorization? Would this require a full redesign of the difficulty labels and the lookup table, and what would that process entail?\n\nLIMITATIONS:\n### 1. Reliance on Simulation\nThe entire evaluation of the DA-SIP framework is conducted within simulated environments. This presents a significant limitation, as there is no evidence of how the system would perform on a physical robot. The \"sim-to-real\" gap can be substantial in robotics, involving challenges that are not fully captured in simulation, such as:\n* **Sensor Noise:** Real-world RGB-D cameras have noise, artifacts, and are affected by lighting conditions, which could degrade the performance of the difficulty classifier.\n* **Physical Dynamics:** The complexities of real-world physics, friction, and object interactions might require different computational allocations than what was found to be optimal in the simulated tasks.\n* **System Latency:** The reported computational speedups do not account for real-world system latencies, such as network communication and actuator response times, which could diminish the overall efficiency gains.\n\n### 2. Manual and Task-Specific Design\nThe framework's effectiveness appears to hinge on several manually designed components that may not generalize well to new tasks or environments.\n* **Fixed Difficulty Categories:** The system uses a predefined set of six difficulty categories (e.g., \"Initial,\" \"Grabbing,\" \"Continuous pushing\"). This discrete categorization was tailored to the specific manipulation tasks in the benchmark. For a new task that doesn't fit this mold (e.g., dexterous in-hand manipulation, pouring), this entire taxonomy would likely need to be redesigned.\n* **Hand-Tuned Lookup Table:** The mapping between a difficulty category and a specific computational triplet (inference steps, solver, and integration mode) seems to be based on heuristics and prior experiments rather than a learned or optimized process. This makes the system brittle; adapting it to a new robot, new objects, or new tasks would require a labor-intensive process of re-tuning this critical lookup table.\n\n### 3. Data Annotation Bottleneck\nThe best-performing difficulty classifier is a supervised lightweight CNN, which required a dataset of approximately 20,000 human-labeled states. This introduces a major practical limitation:\n* **Scalability:** The need to collect and manually annotate a large dataset for every new environment or set of tasks is a significant bottleneck. It undermines the goal of creating an efficient, easily deployable system.\n* **Subjectivity of Labels:** The \"difficulty\" labels are based on human consensus. However, what a human perceives as difficult may not perfectly align with what the robot policy finds computationally challenging, potentially leading to suboptimal resource allocation.\n\n### 4. Lack of Explicit Safety Mechanisms\nThe paper mentions that future work will involve coupling the system with safety monitors. This implies that the current framework lacks explicit safety guarantees. An incorrect classification from the difficulty module could lead to the system allocating insufficient computation for a critical, high-precision action, potentially resulting in task failure or unsafe behavior, especially in contact-rich scenarios. The system's performance is highly dependent on the classifier's accuracy, but there is no fallback or safety layer discussed if the classifier is wrong.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}