arXiv:2603.22430v2 Announce Type: replace Abstract: 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 informat
The paper introduces a significant methodological improvement in offline reinforcement learning by leveraging differentiable world models for inference-time adaptation, addressing limitations of existing approaches.
This advancement could lead to more robust and adaptive AI systems that learn efficiently from fixed datasets, accelerating deployment in complex, real-world environments without continuous retraining.
The ability to adapt policies at inference time using learned world models makes offline RL more practical and closer to real-world operational needs, especially for autonomous systems.
- · AI researchers
- · Robotics companies
- · Autonomous vehicle developers
- · Logistics and manufacturing industries
- · Companies relying on constant retraining for RL deployments
- · Methods lacking inference-time adaptation
More efficient and reliable deployment of learned policies in various applications without needing continuous online interaction.
Accelerated development of more complex autonomous AI agents capable of handling unforeseen circumstances through adaptive planning.
Enhanced automation across sectors, potentially displacing certain human labor roles as AI systems become more robust and self-correcting.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG