Much Ado About Noising: Dispelling the Myths of Generative Robotic Control

• URL: http://arxiv.org/abs/2512.01809v2
• Date: Fri, 05 Dec 2025 21:56:00 GMT
• Title: Much Ado About Noising: Dispelling the Myths of Generative Robotic Control
• Authors: Chaoyi Pan, Giri Anantharaman, Nai-Chieh Huang, Claire Jin, Daniel Pfrommer, Chenyang Yuan, Frank Permenter, Guannan Qu, Nicholas Boffi, Guanya Shi, Max Simchowitz,
• Abstract summary: We evaluate popular generative control policies (GCPs) on common behavior cloning benchmarks.<n>We find that GCPs do not owe their success to their ability to capture multi-modality or to express more complex observation-to-action mappings.<n>Our results suggest that the distribution-fitting component of GCPs is less salient than commonly believed.
• Score: 26.71643888212995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models, like flows and diffusions, have recently emerged as popular and efficacious policy parameterizations in robotics. There has been much speculation as to the factors underlying their successes, ranging from capturing multi-modal action distribution to expressing more complex behaviors. In this work, we perform a comprehensive evaluation of popular generative control policies (GCPs) on common behavior cloning (BC) benchmarks. We find that GCPs do not owe their success to their ability to capture multi-modality or to express more complex observation-to-action mappings. Instead, we find that their advantage stems from iterative computation, as long as intermediate steps are supervised during training and this supervision is paired with a suitable level of stochasticity. As a validation of our findings, we show that a minimum iterative policy (MIP), a lightweight two-step regression-based policy, essentially matches the performance of flow GCPs, and often outperforms distilled shortcut models. Our results suggest that the distribution-fitting component of GCPs is less salient than commonly believed, and point toward new design spaces focusing solely on control performance. Project page: https://simchowitzlabpublic.github.io/much-ado-about-noising-project/

Related papers

• Agentic Test-Time Scaling for WebAgents [65.5178428849495]
  We present Confidence-Aware Test-Time Scaling (CATTS), which uses vote-derived uncertainty to allocate compute only when decisions are genuinely contentious.<n>CATTS improves performance on WebArena-Lite and GoBrowse by up to 9.1% over React while using up to 2.3x fewer tokens than uniform scaling.
  arXiv  Detail & Related papers  (2026-02-12T18:58:30Z)
• Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning [61.380634253724594]
  Large-scale autoregressive models pretrained on next-token prediction and finetuned with reinforcement learning (RL)<n>We show that it is possible to overcome this problem by acting and exploring within the internal representations of an autoregressive model.
  arXiv  Detail & Related papers  (2025-12-23T18:51:50Z)
• Scalable Offline Model-Based RL with Action Chunks [60.80151356018376]
  We study whether model-based reinforcement learning can provide a scalable recipe for tackling complex, long-horizon tasks in offline RL.<n>We call this recipe textbfModel-Based RL with Action Chunks (MAC).<n>We show that MAC achieves the best performance among offline model-based RL algorithms, especially on challenging long-horizon tasks.
  arXiv  Detail & Related papers  (2025-12-08T23:26:29Z)
• Improving Generative Behavior Cloning via Self-Guidance and Adaptive Chunking [29.920087317401396]
  Generative Behavior Cloning is a simple yet effective framework for robot learning.<n>We propose two novel techniques to enhance the consistency and reactivity of diffusion policies.<n>Our approach substantially improves GBC performance across a wide range of simulated and real-world robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-14T11:16:34Z)
• Compose Your Policies! Improving Diffusion-based or Flow-based Robot Policies via Test-time Distribution-level Composition [52.232968183793986]
  General Policy Composition (GPC) is a training-free method that enhances performance by combining the distributional scores of multiple pre-trained policies.<n>GPC consistently improves performance and adaptability across a diverse set of tasks.
  arXiv  Detail & Related papers  (2025-10-01T16:05:53Z)
• GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning [34.25769740497309]
  GenPO is a generative policy optimization framework that leverages exact diffusion inversion to construct invertible action mappings.<n>GenPO is the first method to successfully integrate diffusion policies into on-policy RL, unlocking their potential for large-scale parallelized training and real-world robotic deployment.
  arXiv  Detail & Related papers  (2025-05-24T15:57:07Z)
• IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via Implicit Maximum Likelihood Estimation [3.7584322469996896]
  IMLE Policy is a novel behaviour cloning approach based on Implicit Maximum Likelihood Estimation (IMLE)<n>It excels in low-data regimes, effectively learning from minimal demonstrations and requiring 38% less data on average to match the performance of baseline methods in learning complex multi-modal behaviours.<n>We validate our approach across diverse manipulation tasks in simulated and real-world environments, showcasing its ability to capture complex behaviours under data constraints.
  arXiv  Detail & Related papers  (2025-02-17T23:22:49Z)
• Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling [51.38330727868982]
  We show how action chunking impacts the divergence between a learner and a demonstrator.<n>We propose Bidirectional Decoding (BID), a test-time inference algorithm that bridges action chunking with closed-loop adaptation.<n>Our method boosts the performance of two state-of-the-art generative policies across seven simulation benchmarks and two real-world tasks.
  arXiv  Detail & Related papers  (2024-08-30T15:39:34Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.