Online Fine Tuning

Online fine-tuning refines pre-trained models, often from offline reinforcement learning (RL) or imitation learning datasets, using limited online interaction to improve performance and adapt to new environments or tasks. Current research emphasizes efficient exploration strategies, such as those incorporating uncertainty estimation or intrinsic rewards, to overcome distribution shifts between offline data and online experience, often within model-based or off-policy RL frameworks. This approach is crucial for sample-efficient learning in high-stakes applications like robotics and autonomous systems, where extensive online data collection is impractical or unsafe, and is driving advancements in both theoretical understanding and practical deployment of RL agents.