The Three Regimes of Offline-to-Online Reinforcement Learning

2025-10-01

AI Summary

This paper addresses the core challenge of inconsistent online fine-tuning performance in offline-to-online reinforcement learning. We propose the “stability–plasticity” principle and introduce, for the first time, a tripartite mechanistic framework that categorizes online adaptation into three paradigms—conservative update, balanced adaptation, and aggressive optimization—based on the relative performance of the pre-trained policy versus the offline dataset. This framework systematically guides the trade-off between knowledge retention and policy improvement. Through large-scale empirical evaluation across 63 tasks, we demonstrate its strong explanatory and predictive power: results align with theoretical predictions in 45 cases. The framework significantly enhances algorithmic interpretability and generalizability, establishing the first principled design guideline for offline-to-online transfer.

Technology Category

• Machine Learning: Online Learning & Bandits
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Explains inconsistent offline-to-online RL fine-tuning behavior
• Identifies three regimes requiring distinct stability properties
• Validates framework through large-scale empirical study alignment

Innovation

• Stability-plasticity principle for policy preservation
• Three distinct regimes for online fine-tuning
• Large-scale empirical validation of framework predictions