AGENTIC-SYSTEMSMay 14, 20261 min read11 views

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

Researchers introduce VegAS, a test-time framework that enhances embodied AI agents' robustness by using a generative verifier to select the best action from multiple candidates, achieving up to 36% performance gains.

Computer Science > Artificial Intelligence

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

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.

Source: arXiv cs.AI Recent

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