arXiv cs.AI· Jorge de Freitas, Christos Ziakas, Alessandra Russo·· 14 小时前AI 评分29
世界模型中的 Critic 引导合成经验生成（CGSE）
Generating Critic-Guided Synthetic Experience in Latent World Models
AI 导读
研究者提出 Critic-Guided Synthetic Experience（CGSE），将世界模型作为局部状态转移生成器，用 critic 引导在模型内选择动作以生成合成经验。
正文 · 原文
Abstract:Learning from offline datasets is limited in scalability, as collecting new experience is often costly or unsafe in the real world. World models offer an alternative way to generate new experience through a learned dynamics model, yet learning policies in latent space is limited due to compounding errors in imagined rollouts. To this end, we propose using the world model as a local transition generator, leveraging critic guidance to choose actions inside that model to generate synthetic experiences. We study action refinement methodologies, introducing Critic-Guided Synthetic Experience (CGSE) generation in world models. Specifically, CGSE augments an existing offline dataset by sampling candidate actions using an offline policy and refining each to maximize a first-order approximation of the $Q$-function within a trust region. We then use a frozen latent world model to imagine the future state given each updated action, and use the critic to choose the best one, appending this synthetic transition to the replay buffer. Our method requires no additional real-environment interaction or test-time planner, as it generates synthetic experience offline using the world model. On three OGBench manipulation tasks, CGSE improves an offline policy by a relative improvement of $7.1\%$ on the average success rate.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39100 [cs.LG] |
| (or arXiv:2610.39100v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39100 arXiv-issued DOI via DataCite |
Submission history
From: Jorge De Freitas [view email]
[v1]
Thu, 29 Oct 2026 00:41:38 UTC (646 KB)
来源：arXiv cs.AI · arxiv.org