The article discusses advancements in Reinforcement Learning from Human Feedback (RLHF) techniques and their applications in language model training. It highlights the instability and memory intensity of existing models during training, with a focus on reward model-assisted methods like Reward-model Augmented Supervised Fine-Tuning (RAFT) and RRHF. These techniques aim to enhance the efficiency of offline preference learning by ranking model outputs based on reward signals and optimizing through fine-tuning. The paper also categorizes related work by considering the use of Supervised Fine-Tuning (SFT) and contrastive losses in both offline and online contexts.
"The innovation of RLHF has transformed how language models align with human preferences, yet the training process remains unstable and memory-intensive, necessitating all models to reside on-device."
"Reward-model Augmented SFT introduces techniques to improve offline preference learning by using reward models to filter training data, demonstrating practical efficiency in ranking responses and fine-tuning outputs."
"Emerging methods like RAFT and RRHF streamline the process of offline preference learning, whereby multiple outputs from a policy are generated, ranked, and the best are used for further fine-tuning."
"The landscape of related work is categorized based on the application of SFT or contrastive losses, distinguishing between offline and online update settings in preference learning."
#reinforcement-learning #language-models #human-preferences #offline-learning #artificial-intelligence
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