Coupled Local and Global World Models for Efficient First Order RL

• URL: http://arxiv.org/abs/2602.06219v1
• Date: Thu, 05 Feb 2026 21:57:41 GMT
• Title: Coupled Local and Global World Models for Efficient First Order RL
• Authors: Joseph Amigo, Rooholla Khorrambakht, Nicolas Mansard, Ludovic Righetti,
• Abstract summary: This paper introduces a method that bypasses simulators entirely, training RL policies inside world models learned from robots' interactions with real environments.<n>At its core, our approach enables policy training with large-scale diffusion models via a novel decoupled first-order gradient (FoG) method.<n>We demonstrate the efficacy of our method on the Push-T manipulation task, where it significantly outperforms PPO in sample efficiency.
• Score: 10.305209288475817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World models offer a promising avenue for more faithfully capturing complex dynamics, including contacts and non-rigidity, as well as complex sensory information, such as visual perception, in situations where standard simulators struggle. However, these models are computationally complex to evaluate, posing a challenge for popular RL approaches that have been successfully used with simulators to solve complex locomotion tasks but yet struggle with manipulation. This paper introduces a method that bypasses simulators entirely, training RL policies inside world models learned from robots' interactions with real environments. At its core, our approach enables policy training with large-scale diffusion models via a novel decoupled first-order gradient (FoG) method: a full-scale world model generates accurate forward trajectories, while a lightweight latent-space surrogate approximates its local dynamics for efficient gradient computation. This coupling of a local and global world model ensures high-fidelity unrolling alongside computationally tractable differentiation. We demonstrate the efficacy of our method on the Push-T manipulation task, where it significantly outperforms PPO in sample efficiency. We further evaluate our approach through an ego-centric object manipulation task with a quadruped. Together, these results demonstrate that learning inside data-driven world models is a promising pathway for solving hard-to-model RL tasks in image space without reliance on hand-crafted physics simulators.

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