Generative Reward Models: Merging the Power of RLHF and RLAIF for Smarter AI
In this video we dive into Generative Reward Models, introduced in a fascinating recent AI research paper by Stanford University. As the name implies, Generative Reward Models (GenRM) represent a potential improvement to the reward models currently used in Reinforcement Learning.
Notably, some authors of this paper are also behind the Direct Preference Optimization (DPO) paper, which does not rely on a reward model. However, recent research indicates that solely using non-reward model methods may not be optimal for all scenarios.
Large language models (LLMs) are enhanced through Reinforcement Learning from Human Feedback (RLHF) or Reinforcement Learning from AI Feedback (RLAIF), both of which typically utilize reward models to train on feedback from humans or AI systems.
We start the video with a brief background on Large Language Models (LLMs) training process, specifically focusing on RLHF and RLAIF.
The Generative Reward Models (GenRM) paper suggests a unified approach that combines RLHF and RLAIF together, to generalize better on out-of-distribution data, and to create reward models that are better aligned with user feedback.
An important method which is used to create GenRMs is Self-Taught Reasoner (STaR), which we also review in the video.
Paper page - https://arxiv.org/abs/2410.12832
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Chapters:
0:00 Introduction
0:46 LLM Training
2:08 RLHF & RLAIF
4:00 Generative Reward Models (GenRM)
5:34 Self-Taught Reasoner (STaR)
7:05 Results