arXiv:2605.11151v2 Announce Type: replace Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key challenge, however, is learning an accurate critic in large state--action spaces with limited dataset coverage. To mitigate harmful updates from value overestimation, prior methods impose pessimism by down-weighting out-of-distribution (OOD) actions relative to dataset actions. While effective, this essentially acts as a behavior cloning anchor and can hinder downstream online policy improvement when
The continuous drive to enhance AI learning efficiency and robustness, especially in reinforcement learning, is leading to innovations like offline-to-online methods to bridge simulation and real-world performance gaps.
Improving sample efficiency and the ability to learn from limited, pre-collected data will accelerate AI development and deployment, particularly in complex domains where online interaction is costly or risky.
Reinforcement learning systems can now more effectively leverage existing datasets while mitigating the risks of out-of-distribution actions, potentially leading to faster and safer policy improvements in real-world applications.
- · AI researchers and developers
- · Robotics companies
- · Industries using autonomous systems
- · Developers of AI agents
- · Traditional RL methods requiring extensive online interaction
- · Companies without access to large, diverse offline datasets
More robust and sample-efficient reinforcement learning algorithms become available for deployment.
Accelerated development and adoption of autonomous systems and AI agents in various sectors due to lower training costs and improved safety.
Increased competition in AI development as barriers to entry related to data collection and training efficiency are reduced.
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Read at arXiv cs.AI