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

• URL: http://arxiv.org/abs/2605.12620v1
• Date: Tue, 12 May 2026 18:08:24 GMT
• Title: Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
• Abstract summary: Verifier-Guided Action Selection (VegAS) is a test-time framework designed to improve robustness of MLLM-based embodied agents.<n>We show that VeGAS consistently improves generalization, achieving up to a 36% relative performance gain over strong CoT baselines.
• Score: 23.69429098915734
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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