Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment

Moritz Zoellner; Reece O'Mahoney; Ioannis Havoutis; Rohan Paleja

cs.ROSep 29, 2026

Reactive Real-Time Flow Policies via Asynchronous Distribution Alignment

Authors: Moritz Zoellner, Reece O'Mahoney, Ioannis Havoutis, Rohan Paleja

Organizations: Department of Computer Science, Purdue University · Oxford Robotics Institute, University of Oxford

Abstract

Generalist robot policies such as vision-language-action models (VLAs) have achieved remarkable generalization, but their inference delays can conflict with the demands of real-time control. Asynchronous execution avoids pauses between action chunks by predicting the next sequence of actions while the robot carries out the previous one. In this paper, we study whether asynchronous execution produces the same action distribution as the original VLA. We find that, for non-Markovian demonstrations, asynchronous execution can produce a fundamentally different action distribution, which can limit the policy's reactivity. In our method, we seek to restore this reactivity by aligning the asynchronously produced action distribution with that of the original VLA through two complementary mechanisms. First, Recursive Flow-Field Distillation trains the asynchronous policy using the VLA's action-generation flow. We characterize the learned distribution theoretically and show experimentally that our asynchronous policy can generate nearly the full range of actions the original VLA would produce, while existing asynchronous methods recover only a fraction of that range. Second, Propose-Resolve prepares multiple action sequences asynchronously and uses the latest observation to select among them based on a lightweight approximation of their likelihood under the VLA's action distribution. Our resulting method matches the original VLA's success on LIBERO and retains about 80% of its success on RoboMimic, about 30 percentage points more than existing asynchronous methods.

Figures & tables

Figure 1: Asynchronous execution and distribution alignment. The point clouds schematically represent different action distributions at

t

, with each point corresponding to a possible action sample; inference of such a sample takes

\Delta t

. (A) Synchronous execution queries the VLA at time

t

, so execution has to wait until the action is generated at

t+\Delta t

. (B–C) Asynchronous execution instead starts generation at

t-\Delta t

, while the robot carries out its committed actions, so that the next chunk is prepared for execution at

t

. (B) Real-Time Chunking produces a narrower subset of the actions compared to the VLA when the training data is non-Markovian. (C) Recursive Flow-Field Distillation trains the asynchronous policy using the VLA’s action-generation flow, recovering a broader proposal distribution. (D) Propose–Resolve uses the latest observation to estimate each candidate’s likelihood under the corresponding VLA distribution and selects the highest-scoring continuation.

Figure 2: Execution timing and action distributions at handoff. Schematic with

d=3

and

s=5

. Left: Synchronous execution queries the VLA at time

t

for the five actions executed over

[t,t+s]

. Asynchronous execution instead begins generation at

t-d

: the first

d

actions form the committed prefix

P

, while the following

s

actions form the continuation

U

executed after handoff. Note that the schematic assumes prefix-compatible generation, so

U

begins from the state reached after executing

P

; enforcing this compatibility is the original objective from RTC. Right: The resulting distributions over the same

s

-step execution interval. Black curves show possible VLA action chunks conditioned on

o^{+}

, while red curves show asynchronous continuations generated from

(o^{-},P)

Figure 3: One ToolHang handoff at $d=10$ .

Figure 4: Distribution comparison at

o^{+}

. For each handoff tuple

(o^{-},P,o^{+})

d=10

, we draw 128 continuations from each asynchronous method conditioned on

(o^{-},P)

and independent banks of 128 support and 128 query continuations from

\pi_{\mathrm{VLA}}(\cdot\mid o^{+})

. Left: Per-state precision and recall over

N=2{,}775

ToolHang demonstration handoffs (

291

for RTC), shown as histograms. Right: Mean precision and recall across RoboMimic and LIBERO, evaluated on both demonstration states and states from VLA rollouts. Appendix D details how precision and recall are computed, extends this analysis across delays

d

, and additionally reports the distribution obtained under the teacher-sample supervision introduced in Section 4.1 .

Figure 5: Real-time execution success and ablation. Top: Rollout success on LIBERO and RoboMimic across asynchronous delays, compared with the synchronous VLA reference. Our method uses

K=32

RFD proposals followed by Propose–Resolve. Bottom: Ablation at

d=10

, comparing each asynchronous proposer with a single continuation (

K=1

) and with Propose–Resolve over

K=32

candidates.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6: Distribution alignment across delays. The support comparison from Figure 4 , extended across delays

d=0,\ldots,10

on ToolHang, Square, and Transport, showing how relative recall and precision evolve on demonstration and rollout states.

Figure 7: Action support with teacher-sample supervision. The ToolHang comparison from Figure 4 , now including teacher-sample supervision (TSS) and reporting precision and recall relative to each state’s VLA self-coverage.

Hyperparameter	Base VLA / TT-RTC / RFD	Resolver
Optimizer	AdamW	AdamW
Adam $(\beta_{1},\beta_{2})$	$(0.9,0.95)$	$(0.9,0.95)$
Weight decay	$10^{-10}$	$10^{-4}$
Gradient clipping norm	10	5
Learning-rate schedule	Cosine with warm-up	Cosine with warm-up
Warm-up steps	1,000	500

Appendix

Table 1: Optimization settings. Shared settings for base-VLA fine-tuning, TT-RTC, RFD, and resolver training.

Component	Task	Peak LR	Batch	Updates	Seed
Base VLA	Square	$10^{-4}$	16	60,000	1000
Base VLA	Transport	$10^{-4}$	8	60,000	1000
Base VLA	ToolHang	$10^{-4}$	16	60,000	1000
Base VLA	LIBERO	—	—	Released checkpoint	—
TT-RTC	Square	$10^{-4}$	16	100,000	1000
TT-RTC	Transport	$2.5\times 10^{-5}$	16	25,000	1000

Appendix

Table 2: Training configurations. Policy update counts extend through the evaluated checkpoint; resolver counts give the full training budget. Learning rates are configured peaks. Arrows and sums denote successive stages.

Model	$K$	Propose mean / p99	Resolve mean / p99	Total mean
SmolVLA	1	27.0 / 27.6	0 / 0	27.0
SmolVLA	8	39.8 / 40.4	12.0 / 12.4	51.8
SmolVLA	16	56.5 / 57.1	12.0 / 12.5	68.6
SmolVLA	32	90.8 / 91.4	12.0 / 12.3	102.8

Appendix

Table 3: Compute latency of Propose–Resolve. RFD proposals with energy-argmax resolution on an NVIDIA GeForce RTX 4090, using 50-action chunks and ten flow steps. All times are in milliseconds. Proposal generation overlaps with execution; resolution follows observation of

o^{+}

. At

K=1

, resolution is bypassed. Total means sum separately measured proposal and resolution means; they are not joint pipeline measurements.

Explore similar work

DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs
May 19, 2026Yixiang Zhu, Yonghao Chen, Zijie Yang +2Counterfactual Learning Inference Latency
The Hong Kong University of Science and Technology (Guangzhou) · One Robotics
FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking
Jul 27, 2026Hai Jiang, Yixian Zou, Binbin Liang +3Action Prediction Robot Systems
School of Aeronautics and Astronautics, Sichuan University · University of Electronic Science and Technology of China · University of Alberta
SmoothRL: Online Reinforcement Learning During Asynchronous Execution
Aug 30, 2026Guang Gao, Yuxuan Nong, Baifu Huang +1Asynchronous Execution Offline Reinforcement Learning
Astribot Team

May 19, 2026, cs.ROJ/K move · Enter open · S save

DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs

Yixiang Zhu, Yonghao Chen, Zijie Yang, Yusong Hu+1

The Hong Kong University of Science and Technology (Guangzhou) · One Robotics

Counterfactual Learning Inference Latency Fast Inference LLM Defense Mechanisms

Open paper AskPDF arXiv

Vision-Language-Action (VLA) policies increasingly rely on asynchronous inference to hide large-model latency behind ongoing robot motion. While this avoids the stop-and-go behavior of synchronous action-chunk execution, it creates a prediction-execution mismatch: the next chunk is computed from a stale observation at inference start but executed only after the robot and scene have evolved. As a result, actions that fit the prediction-time state can become misaligned with the execution-time state. Existing runtime repair, behavior-cloning, and preference-alignment approaches do not directly teach the policy to resolve this stale-input mismatch. We propose DEFLECT, an offline post-training framework for delay-robust asynchronous VLAs. DEFLECT converts latency-induced mismatch into counterfactual preference supervision: a frozen reference VLA generates a preferred chunk from the future execution-time observation and a rejected chunk from the stale prediction-time observation. The trainable policy scores both chunks under the same deployment-time input, learning to favor execution-time-aligned actions while a supervised fine-tuning anchor preserves the expert action manifold. DEFLECT requires no human preference labels, reward models, online robot rollouts, architectural changes, or additional inference-time computation. Across Kinetix, LIBERO, and three real-robot tasks, DEFLECT improves delay robustness over strong asynchronous VLA baselines, raising high-latency success by up to 6.4 percentage points and achieving a 4.6 percentage-point gain at the longest delay on a real-scale VLA.

Abstract

Explore similar work

DEFLECT: Temporal Counterfactual Preference Learning for Delay-Robust Asynchronous VLAs

FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking

SmoothRL: Online Reinforcement Learning During Asynchronous Execution