RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models

• URL: http://arxiv.org/abs/2506.17811v2
• Date: Mon, 07 Jul 2025 02:08:10 GMT
• Title: RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
• Authors: Jacky Kwok, Christopher Agia, Rohan Sinha, Matt Foutter, Shulu Li, Ion Stoica, Azalia Mirhoseini, Marco Pavone,
• Abstract summary: We introduce RoboMonkey, a test-time scaling framework for Vision-Language-Action (VLA) models.<n>RoboMonkey samples a small set of actions from a VLA, applies Gaussian perturbation and majority voting to construct an action proposal distribution, and then uses a Vision Language Model (VLM)-based verifier to select the optimal action.<n>We show that pairing existing VLAs with RoboMonkey yields significant performance gains, achieving a 25% absolute improvement on out-of-distribution tasks and 9% on in-distribution tasks.
• Score: 28.422082187079166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in visuomotor control, yet ensuring their robustness in unstructured real-world environments remains a persistent challenge. In this paper, we investigate test-time scaling through the lens of sampling and verification as means to enhance the robustness and generalization of VLAs. We first demonstrate that the relationship between action error and the number of generated samples follows an exponentiated power law across a range of VLAs, indicating the existence of inference-time scaling laws. Building on these insights, we introduce RoboMonkey, a test-time scaling framework for VLAs. At deployment, RoboMonkey samples a small set of actions from a VLA, applies Gaussian perturbation and majority voting to construct an action proposal distribution, and then uses a Vision Language Model (VLM)-based verifier to select the optimal action. We propose a synthetic data generation pipeline for training such VLM-based action verifiers, and demonstrate that scaling the synthetic dataset consistently improves verification and downstream accuracy. Through extensive simulated and hardware experiments, we show that pairing existing VLAs with RoboMonkey yields significant performance gains, achieving a 25% absolute improvement on out-of-distribution tasks and 9% on in-distribution tasks. Additionally, when adapting to new robot setups, we show that fine-tuning both VLAs and action verifiers yields a 7% performance increase compared to fine-tuning VLAs alone.

Related papers

• Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment [58.93227458806748]
  CoVer-VLA is a hierarchical test-time verification pipeline using the trained verifier.<n>Our framework precomputes a diverse set of rephrased instructions from a Vision-Language-Model.<n>It repeatedly generates action candidates for each instruction, and then uses the verifier to select the optimal high-level prompt and low-level action chunks.
  arXiv  Detail & Related papers  (2026-02-12T18:59:59Z)
• Steering Vision-Language-Action Models as Anti-Exploration: A Test-Time Scaling Approach [78.4812458793128]
  We propose textbfTACO, a test-time-scaling framework that applies a lightweight pseudo-count estimator as a high-fidelity verifier of action chunks.<n>Our method resembles the classical anti-exploration principle in offline reinforcement learning (RL), and being gradient-free, it incurs significant computational benefits.
  arXiv  Detail & Related papers  (2025-12-02T14:42:54Z)
• Verifier-free Test-Time Sampling for Vision Language Action Models [46.54221299065429]
  Masking Distribution Guided Selection (MG-Select) is a novel test-time scaling framework for Vision-Language-Action models.<n>Our experiments demonstrate that MG-Select achieves significant performance improvements.
  arXiv  Detail & Related papers  (2025-10-07T08:38:08Z)
• Align-Then-stEer: Adapting the Vision-Language Action Models through Unified Latent Guidance [63.33213516925946]
  We introduce textbfAlign-Then-stEer (textttATE), a novel, data-efficient, and plug-and-play adaptation framework.<n>Our work presents a general and lightweight solution that greatly enhances the practicality of deploying VLA models to new robotic platforms and tasks.
  arXiv  Detail & Related papers  (2025-09-02T07:51:59Z)
• Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance [27.47568622830058]
  This work introduces Spec-VLA, an SD framework designed to accelerate Vision-Language-Action (VLA) models.<n>To boost the generation speed, we propose an effective mechanism to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model.
  arXiv  Detail & Related papers  (2025-07-30T07:04:09Z)
• Group Relative Augmentation for Data Efficient Action Detection [11.169883977958454]
  Adapting large Video-Language Models (VLMs) for action detection using only a few examples poses challenges.<n>We propose an efficient adaptation strategy combining parameter-efficient tuning (LoRA) with a novel learnable internal feature augmentation.<n>We demonstrate our method's effectiveness on complex multi-label, multi-person action detection datasets.
  arXiv  Detail & Related papers  (2025-07-28T21:46:05Z)
• EdgeVLA: Efficient Vision-Language-Action Models [0.4005096060512278]
  This paper introduces Edge VLA, a novel approach designed to significantly enhance the inference speed of Vision-Language-Action (VLA) models.<n>We achieve this through two key innovations: 1) Eliminating the autoregressive requirement for end-effector position prediction, leading to a 7x speedup in inference, and 2) Leveraging the efficiency of Small Language Models (SLMs)<n>Our early results demonstrate that EVLA achieves comparable training characteristics to OpenVLA while offering substantial gains in inference speed and memory efficiency.
  arXiv  Detail & Related papers  (2025-07-18T16:15:09Z)
• CronusVLA: Transferring Latent Motion Across Time for Multi-Frame Prediction in Manipulation [67.1520483301709]
  CronusVLA is a unified framework that extends single-frame VLA models to the multi-frame paradigm through an efficient post-training stage.<n>CronusVLA achieves state-of-the-art performance on SimplerEnv with 70.9% success rate, and 12.7% improvement over OpenVLA on LIBERO.
  arXiv  Detail & Related papers  (2025-06-24T17:30:27Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [69.54069477520534]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models [89.44024245194315]
  We introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs)<n>We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens.<n>Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T22:23:04Z)
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success [100.226572152954]
  We present an optimized fine-tuning recipe for vision-language-action models (VLAs)<n>Our recipe boosts OpenVLA's average success rate across four task suites from 76.5% to 97.1% while increasing action generation throughput by 26$times$.<n>In real-world evaluations, our fine-tuning recipe enables OpenVLA to successfully execute dexterous, high-frequency control tasks on a bimanual ALOHA robot.
  arXiv  Detail & Related papers  (2025-02-27T00:30:29Z)
• CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation [100.25567121604382]
  Vision-Language-Action (VLA) models have improved robotic manipulation in terms of language-guided task execution and generalization to unseen scenarios.<n>We present a new advanced VLA architecture derived from Vision-Language-Models (VLM)<n>We show that our model not only significantly surpasses existing VLAs in task performance and but also exhibits remarkable adaptation to new robots and generalization to unseen objects and backgrounds.
  arXiv  Detail & Related papers  (2024-11-29T12:06:03Z)
• LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation [7.8735930411335895]
  Vision-Language-Action (VLA) models are an integrated solution for robotic manipulation tasks. The data-driven nature of VLA models, combined with their lack of interpretability, makes the assurance of their effectiveness and robustness a challenging task. We propose LADEV, a comprehensive and efficient platform specifically designed for evaluating VLA models.
  arXiv  Detail & Related papers  (2024-10-07T16:49:16Z)
• VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation [7.8735930411335895]
  We present VLATest, a fuzzing framework designed to generate robotic manipulation scenes for testing VLA models.<n>Based on VLATest, we conducted an empirical study to assess the performance of seven representative VLA models.
  arXiv  Detail & Related papers  (2024-09-19T16:33:00Z)
• OpenVLA: An Open-Source Vision-Language-Action Model [131.74098076670103]
  We introduce OpenVLA, an open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA shows strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate. We release model checkpoints, fine-tuning notebooks, and our PyTorch with built-in support for training VLAs at scale on Open X-Embodiment datasets.
  arXiv  Detail & Related papers  (2024-06-13T15:46:55Z)

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.