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Reinforcement learning from human feedback (RLHF) has emerged as a reliableapproach to aligning large language models (LLMs) to human preferences. Amongthe plethora of RLHF techniques, proximal policy optimization (PPO) is of themost widely used methods. Despite its popularity, however, PPO may suffer frommode collapse, instability, and poor sample efficiency. We show that theseissues can be alleviated by a novel algorithm that we refer to asAdvantage-Induced Policy Alignment (APA), which leverages a squared error lossfunction based on the estimated advantages. We demonstrate empirically that APAconsistently outperforms PPO in language tasks by a large margin, when aseparate reward model is employed as the evaluator. In addition, compared withPPO, APA offers a more stable form of control over the deviation from themodel's initial policy, ensuring that the model improves its performancewithout collapsing to deterministic output. In addition to empirical results,we also provide a theoretical justification supporting the design of our lossfunction.

Fine-Tuning Language Models with Advantage-Induced Policy Alignment