Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning

Sunshine Jiang; John Marangola; David Zhang; Raghuram Kowdeed; Ruiyang Luo; Nitish Dashora; Richard Li; Pulkit Agrawal; Zhang-Wei Hong

arXiv · 2607.08837

Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning

Sunshine Jiang·John Marangola·David Zhang·Raghuram Kowdeed·Ruiyang Luo·Nitish Dashora·Richard Li·Pulkit Agrawal·Zhang-Wei Hong

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Abstract

Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject stochasticity in the action space, but such jitter only yields rollouts close to the original. Escaping a weak policy often requires global perturbations that action noise cannot produce. Large language models (LLMs) and vision-language-action (VLA) models offer a pathway: they condition the policy on a natural language prompt, and since the rollout follows from it, modifying the prompt induces global changes. The challenge is finding prompts that induce useful global changes. With a weak policy that rarely succeeds, reward is too sparse to select on. Our idea is to refine prompts from the rollouts themselves: a vision-language model (VLM) reasons over the rollout video, diagnoses how the policy responded, and rewrites the prompt to elicit better behavior next time. This procedure resembles posterior sampling, a classical RL exploration framework, at the level of prompts: the VLM maintains an implicit distribution over useful prompts and updates it from observed rollouts. We call this strategy Prompt-Driven Exploration (PDE). Across manipulation and reasoning tasks, PDE enables RL to learn successful policies even from zero-reward starts, and improves sample efficiency more broadly. Our website is available at https://xinyunsunshine.github.io/prompt-rl.

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BibTeX RIS

Sunshine Jiang, John Marangola, David Zhang, Raghuram Kowdeed, Ruiyang Luo, Nitish Dashora, Richard Li, Pulkit Agrawal, Zhang-Wei Hong. 2026-09-18. Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning. https://arxiv.org/abs/2607.08837

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