Prompt-Driven Exploration
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.
- ▪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.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.08837 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
| Last seen | 2026-07-13T04:20:37.625Z |
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Opening excerpt (first ~120 words) tap to expand
Computer Science > Machine Learning arXiv:2607.08837 (cs) [Submitted on 9 Jul 2026] Title:Prompt-Driven Exploration Authors:Sunshine Jiang, John Marangola, David Zhang, Raghuram Kowdeed, Ruiyang Luo, Nitish Dashora, Richard Li, Pulkit Agrawal, Zhang-Wei Hong View a PDF of the paper titled Prompt-Driven Exploration, by Sunshine Jiang and 8 other authors View PDF HTML (experimental) 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.
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