<ul><li>Improving the performance of pre-trained policies through online reinforcement learning is crucial yet challenging.</li><li>Existing online RL fine-tuning methods often require continued training with offline pretrained Q-functions for stability and performance.</li><li>A new method called PORL (Policy-Only Reinforcement Learning Fine-Tuning) has been proposed, which uses only the offline pre-trained policy for efficient online RL fine-tuning.</li><li>PORL rapidly initializes the Q-function from scratch during the online phase to avoid pessimism, achieving competitive performance with advanced offline-to-online RL algorithms.</li></ul>

Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only

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