Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents

TL;DR

This paper introduces VegAS, a verifier-guided framework that enhances the robustness of multimodal large language model-based embodied agents by explicitly verifying and selecting actions during inference.

Contribution

VegAS is a novel test-time approach that uses a generative verifier and data synthesis to improve agent robustness without altering the core policy.

Findings

VegAS achieves up to 36% relative performance improvement on challenging tasks.

Using an off-the-shelf LLM as verifier yields no benefit, motivating data synthesis.

Consistently improves generalization across Habitat and ALFRED benchmarks.

Abstract

Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly advanced the reasoning capabilities of such agents through strong vision-language knowledge and chain-of-thought (CoT) reasoning, yet remain brittle when faced with challenging out-of-distribution scenarios. To address this, we propose Verifier-Guided Action Selection (VegAS), a test-time framework designed to improve the robustness of MLLM-based embodied agents through an explicit verification step. At inference time, rather than committing to a single decoded action, VeGAS samples an ensemble of candidate actions and uses a generative verifier to identify the most reliable choice, without modifying the underlying policy. Crucially, we find that using an MLLM off-the-shelf as a verifier yields no improvement, motivating our LLM-driven data synthesis strategy, which automatically constructs a diverse curriculum of failure cases to expose the verifier to a rich distribution of potential errors at training time. Across embodied reasoning benchmarks spanning the Habitat and ALFRED environments, VeGAS consistently improves generalization, achieving up to a 36% relative performance gain over strong CoT baselines on the most challenging multi-object, long-horizon tasks.