KL-Regularised Q-Learning: A Token-level Action-Value perspective on Online RLHF
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908500068663296 |
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| author | Brown, Jason R Wells, Lennie Young, Edward James Bacallado, Sergio |
| author_facet | Brown, Jason R Wells, Lennie Young, Edward James Bacallado, Sergio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17000 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KL-Regularised Q-Learning: A Token-level Action-Value perspective on Online RLHF Brown, Jason R Wells, Lennie Young, Edward James Bacallado, Sergio Computation and Language Machine Learning 68T07 I.2.6; I.2.8 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. |
| title | KL-Regularised Q-Learning: A Token-level Action-Value perspective on Online RLHF |
| topic | Computation and Language Machine Learning 68T07 I.2.6; I.2.8 |
| url | https://arxiv.org/abs/2508.17000 |