EVE: A Generator-Verifier System for Generative Policies

TL;DR

EVE is a modular framework that enhances pretrained generative policies for embodied control by employing zero-shot visual verifier agents at test time, significantly improving task success without additional training.

Contribution

Introduces EVE, a generator-verifier system that boosts generative policy performance through test-time verification with zero-shot visual language models, without retraining.

Findings

Consistently improves task success rates across manipulation tasks.

Effectively integrates multiple verifier agents for action refinement.

Provides practical guidelines for scalable generator-verifier system design.

Abstract

Visuomotor policies based on generative architectures such as diffusion and flow-based matching have shown strong performance but degrade under distribution shifts, demonstrating limited recovery capabilities without costly finetuning. In the language modeling domain, test-time compute scaling has revolutionized reasoning capabilities of modern LLMs by leveraging additional inference-time compute for candidate solution refinement. These methods typically leverage foundation models as verification modules in a zero-shot manner to synthesize improved candidate solutions. In this work, we hypothesize that generative policies can similarly benefit from additional inference-time compute that employs zero-shot VLM-based verifiers. A systematic analysis of improving policy performance through the generation-verification framework remains relatively underexplored in the current literature. To this end, we introduce EVE - a modular, generator-verifier interaction framework - that boosts the performance of pretrained generative policies at test time, with no additional training. EVE wraps a frozen base policy with multiple zero-shot, VLM-based verifier agents. Each verifier proposes action refinements to the base policy candidate actions, while an action incorporator fuses the aggregated verifier output into the base policy action prediction to produce the final executed action. We study design choices for generator-verifier information interfacing across a system of verifiers with distinct capabilities. Across a diverse suite of manipulation tasks, EVE consistently improves task success rates without any additional policy training. Through extensive ablations, we isolate the contribution of verifier capabilities and action incorporator strategies, offering practical guidelines to build scalable, modular generator-verifier systems for embodied control.