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

• URL: http://arxiv.org/abs/2508.17000v1
• Date: Sat, 23 Aug 2025 11:50:54 GMT
• Title: KL-Regularised Q-Learning: A Token-level Action-Value perspective on Online RLHF
• Authors: Jason R Brown, Lennie Wells, Edward James Young, Sergio Bacallado,
• Abstract summary: We develop a new action-value RL method for the LM-RLHF setting, KL-regularised Q-Learning (KLQ)<n>We show that our method is equivalent to a version of PPO in a certain specific sense, despite its very different motivation.<n>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.
• Score: 1.8665975431697432
• License: http://creativecommons.org/licenses/by/4.0/
• 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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