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Poster
in
Workshop: 2nd Workshop on Models of Human Feedback for AI Alignment (MoFA)

KL-Regularised Q-Learning: A Token-level Action-Value perspective on Online RLHF

Lennie Wells · Edward J. Young · Jason Brown · Sergio Bacallado


Abstract:

Proximal Policy Optimisation (PPO) is an established and effective policy gradient algorithm used for Language Model Reinforcement Learning from Human Feedback (LM-RLHF). PPO performs well empirically but has a heuristic motivation and handles the KL-divergence constraint used in LM-RLHF in an ad-hoc manner.In this paper, we develop a a new action-value RL method for the LM-RLHF setting, KL-regularised Q-Learning (KLQ).We then show that our method is equivalent to a version of PPO in a certain specific sense, despite its very different motivation.Finally, we benchmark KLQ on two key language generation tasks---summarisation and single-turn dialogue. We demonstrate that KLQ performs on-par with PPO at optimising the LM-RLHF objective, and achieves a consistently higher win-rate against PPO on LLM-as-a-judge evaluations.

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