arXiv:2510.01460v4 Announce Type: replace-cross 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. Guided by the stability--plasticity principle, we propose a framework that can explain this inconsistency: We argue that efficient fine-tuning must preserve the utility of the stronger offline prior, whether that is
The paper provides a framework that addresses the inconsistency in offline-to-online reinforcement learning, which is a critical bottleneck for deploying more robust and adaptable AI systems in dynamic environments.
This research is crucial for advancing AI's practical application, particularly in developing autonomous agents that can learn effectively from both pre-existing data and real-time interaction.
The proposed framework offers a principled approach to designing more effective fine-tuning strategies in offline-to-online RL, potentially leading to more reliable and consistent AI agent performance across varied settings.
- · AI researchers and developers
- · Companies deploying autonomous systems
- · Reinforcement learning applications
- · Approaches lacking principled fine-tuning
- · Systems with high online data dependency exclusively
Improved performance and broader applicability of AI agents in complex, real-world scenarios.
Accelerated development and adoption of AI systems requiring continuous adaptation and learning.
Enhanced trust and reliability in AI-powered automation across various industries due to more predictable learning behavior.
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Read at arXiv cs.AI