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The technique of Reinforcement Learning from Human Feedback (RLHF) is acommonly employed method to improve pre-trained Language Models (LM), enhancingtheir ability to conform to human preferences. Nevertheless, the currentRLHF-based LMs necessitate full retraining each time novel queries or feedbackare introduced, which becomes a challenging task because human preferences canvary between different domains or tasks. Retraining LMs poses practicaldifficulties in many real-world situations due to the significant time andcomputational resources required, along with concerns related to data privacy.To address this limitation, we propose a new method called Continual OptimalPolicy Fitting (COPF), in which we estimate a series of optimal policies usingthe Monte Carlo method, and then continually fit the policy sequence with thefunction regularization. COPF involves a single learning phase and doesn'tnecessitate complex reinforcement learning. Importantly, it shares thecapability with RLHF to learn from unlabeled data, making it flexible forcontinual preference learning. Our experimental results show that COPFoutperforms strong Continuous learning (CL) baselines when it comes toconsistently aligning with human preferences on different tasks and domains.

COPF: Continual Learning Human Preference through Optimal Policy Fitting