Inference Time Policy Optimization for Offline RL with Differentiable World Models
Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without further refinement. Inspired by model predictive control (MPC), we introduce an inference time adaptation framework that utilizes a pretrained policy along with a learned world model. While existing world model and diffusion-planning methods use learned dynamics to generate imagined trajectories during training, or to sample candidate plans at inference time, they do not use inference-time information to *optimize* the policy parameters on the fly. In contrast, our design is a Differentiable World Model (DWM) pipeline that enables end-to-end gr...
| Authors | Rohan Deb, Stephen J. Wright, Arindam Banerjee |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 20 May 2026 |
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