Coupled Local and Global World Models for Efficient First Order RL

Real-world tasks solved zero-shot by policies trained entirely inside our learned world models: Push-T with a tabletop manipulator (left), Ego-Centric Grasp and Lift with a G1 humanoid (centre), and Ego-Centric Push Cube with a Go2 quadruped (right).

Abstract

World models offer a promising avenue for capturing complex environment dynamics where simulators face challenges. However, large-scale world models required for complex real-world settings are computationally expensive to adopt in popular RL approaches. We introduce a novel first-order RL method that enables policy training via a decoupled first-order gradient (FoG): a large-scale world model generates accurate forward trajectories while a lightweight latent-space surrogate approximates its local dynamics for efficient gradient computation. This coupled local-and-global formulation allows high-fidelity forward dynamics alongside the computationally efficient differentiation needed for model-based RL. Across a range of real-world robotic tasks we demonstrate tractable RL and zero-shot deployment, with significantly better sample efficiency than PPO on a canonical real-world Push-T benchmark and similar gains on more complex ego-centric manipulation and grasping.

Method

We extend Decoupled forward-backward Model-based policy Optimization (DMO) to the simulator-free setting in image space. A pretrained large-scale world model (DIAMOND for Push-T, DreamerV4 for ego-centric tasks) acts as the global simulator surrogate. Alongside it, a lightweight Recurrent State-Space Model (RSSM) acts as the local low-dimensional latent dynamics that supplies stable, low-variance gradients without backpropagating through the heavy global model. Forward accuracy and backward tractability are optimised independently, opening the door to practical, efficient FoG-MBRL with large-scale pixel-space world models.

Sample efficiency

On Push-T we match or beat PPO with an order-of-magnitude fewer environment samples, and we see the same pattern on the more complex G1 ego-centric grasp-and-lift and Go2 ego-centric push-cube tasks. All curves are over 4 seeds. Click any figure to enlarge.

Real-robot rollouts

Policies trained entirely inside the learned world models, deployed zero-shot on hardware.

World-model fidelity

A side-by-side rollout on the G1 humanoid grasp-and-lift task lets us inspect how the global diffusion world model and the lightweight local RSSM each track the real trajectory over a 5.5-second horizon. The global model preserves visual fidelity and contact dynamics long after the local model loses high-frequency detail — which is exactly why we keep the global model in the forward pass and reserve the local model for cheap backward gradients.

Patching world-model exploits

When training a high-reward RL policy entirely inside a learned world model, the policy inevitably finds physical hallucinations — frames where the world model breaks its own physics to maximise reward. Our pipeline deploys those exploit policies on the real robot to harvest targeted patching data, then fine-tunes the world model on it. The figure below shows the same exploit policy rolled out on the original and the patched model — the hallucination disappears.

BibTeX

@misc{amigo2026coupledlocalglobalworld,
      title={Coupled Local and Global World Models for Efficient First Order RL},
      author={Joseph Amigo and Rooholla Khorrambakht and Nicolas Mansard and Ludovic Righetti},
      year={2026},
      eprint={2602.06219},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2602.06219},
}