offline-to-online fine-tuning

Offline RL gives you a decent policy without risk; online RL can polish it but is expensive and dangerous from scratch. The natural marriage is to pretrain offline on a fixed dataset, then let the agent interact a little to refine — a safe head start, followed by closing the last gap.

You initialize the value function and policy from offline training and continue with online updates, now collecting fresh data. The friction is that the very conservatism which made offline learning stable can throttle online improvement, and the sudden change in data distribution can cause a brief dip or even a collapse.

Designing this transition gracefully — relaxing pessimism gradually, balancing offline and online data, keeping value estimates calibrated — is an active research area. Methods like AWAC, IQL, and Cal-QL are built specifically to fine-tune without falling apart.