Semi-Supervised Reward Modeling via Iterative Self-Training

• URL: http://arxiv.org/abs/2409.06903v1
• Date: Tue, 10 Sep 2024 22:57:58 GMT
• Title: Semi-Supervised Reward Modeling via Iterative Self-Training
• Authors: Yifei He, Haoxiang Wang, Ziyan Jiang, Alexandros Papangelis, Han Zhao,
• Abstract summary: We propose Semi-Supervised Reward Modeling (SSRM), an approach that enhances RM training using unlabeled data. We demonstrate that SSRM significantly improves reward models without incurring additional labeling costs. Overall, SSRM substantially reduces the dependency on large volumes of human-annotated data, thereby decreasing the overall cost and time involved in training effective reward models.
• Score: 52.48668920483908
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reward models (RM) capture the values and preferences of humans and play a central role in Reinforcement Learning with Human Feedback (RLHF) to align pretrained large language models (LLMs). Traditionally, training these models relies on extensive human-annotated preference data, which poses significant challenges in terms of scalability and cost. To overcome these limitations, we propose Semi-Supervised Reward Modeling (SSRM), an approach that enhances RM training using unlabeled data. Given an unlabeled dataset, SSRM involves three key iterative steps: pseudo-labeling unlabeled examples, selecting high-confidence examples through a confidence threshold, and supervised finetuning on the refined dataset. Across extensive experiments on various model configurations, we demonstrate that SSRM significantly improves reward models without incurring additional labeling costs. Notably, SSRM can achieve performance comparable to models trained entirely on labeled data of equivalent volumes. Overall, SSRM substantially reduces the dependency on large volumes of human-annotated data, thereby decreasing the overall cost and time involved in training effective reward models.

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