A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different ways. This motivates use of sequence-level objectives for dialogue response generation. In this paper, we study the efficacy of various offline reinforcement learning (RL) methods to maximize such objectives. We present a comprehensive evaluation across multiple datasets, models, and metrics. Offline RL shows a clear performance improvement over teacher forcing while not inducing training instability or sacrificing practical training budgets.

翻译：教师强制（TF）是语言模型常用的训练技术，但该技术试图精确匹配人类语言，而相同语义可通过不同表达方式呈现。这促使我们在对话回复生成中采用序列级优化目标。本文系统研究了多种离线强化学习（RL）方法对该类目标最大化的有效性，并在多数据集、多模型和多评估指标下开展综合评估。实验表明，离线强化学习相较于教师强制方法展现出显著性能提升，且不会引发训练不稳定或导致训练预算增加。