REVIEW 6 cited by
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 6 Pith papers
-
Q-VGM: Q-Value-Gradient Matching for Off-Policy Reinforcement Learning of Flow-Matching VLA
Q-VGM offline RL fine-tuning converts critic Q-gradients into residual velocity targets for flow-matching VLAs, raising LIBERO success from 75.0% to 92.5%.
-
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
WAV self-improves action-conditioned world models by cycle-consistent verification of state plausibility and sparse action reachability, doubling sample efficiency and lifting policy reward by over 22% on nine tasks.
-
Verifier-free Test-Time Sampling for Vision-Language-Action Models
A verifier-free test-time sampling method for vision-language-action models that selects actions by KL divergence to a condition-masked reference distribution, improving task success rates.
-
Action Chunk Scheduling for Batched Robot Policy Serving
A lookahead scheduler that simulates each robot's action-queue state before choosing batches improves throughput in heterogeneous multi-robot policy serving by up to 18% in real-world tests.
-
Addressing the Orchestration Gap in Generalist Robots via Physical Agency
A closed-loop inference-time orchestrator using a frontier VLM lifts frozen robot policies from 12.8% to 53.3% on LIBERO-PRO and from near-zero to over 90% on reasoning-limited real-robot tasks, without retraining the...
-
EVE: A Generator-Verifier System for Generative Policies
Zero-shot VLM verifiers, ensembled and fused via guided diffusion, improve frozen generative robot policies' success rates by 1-2 percentage points on simulated manipulation tasks.
Discussion (0). Continue with ORCID to comment.