The Three Regimes of Offline-to-Online Reinforcement Learning

• URL: http://arxiv.org/abs/2510.01460v1
• Date: Wed, 01 Oct 2025 20:58:14 GMT
• Title: The Three Regimes of Offline-to-Online Reinforcement Learning
• Authors: Lu Li, Tianwei Ni, Yihao Sun, Pierre-Luc Bacon,
• Abstract summary: offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning.<n>We propose a stability--plasticity principle that can explain this inconsistency.<n>This work provides a principled framework for guiding design choices in offline-to-online RL based on the relative performance of the offline dataset and the pretrained policy.
• Score: 22.777667142224587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning. However, its empirical behavior is highly inconsistent: design choices of online-fine tuning that work well in one setting can fail completely in another. We propose a stability--plasticity principle that can explain this inconsistency: we should preserve the knowledge of pretrained policy or offline dataset during online fine-tuning, whichever is better, while maintaining sufficient plasticity. This perspective identifies three regimes of online fine-tuning, each requiring distinct stability properties. We validate this framework through a large-scale empirical study, finding that the results strongly align with its predictions in 45 of 63 cases. This work provides a principled framework for guiding design choices in offline-to-online RL based on the relative performance of the offline dataset and the pretrained policy.

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