Teaching AI to Train Itself
How self-rewarding language models recursively improve themselves and potentially unlock superalignment
In the rapidly evolving field of artificial intelligence, a potentially groundbreaking approach to training language models has emerged: Self-Rewarding Language Models (SRLMs). The basic idea is to let the AI makes itself better by acting as a judge of its own outputs.
Early experiments are showing that this technique has promise, but also certain limitations.
In this article, we explore the paper title “Self-Rewarding Language Models” (2024) by Weizhe Yuan at other researchers from Meta and NYU.
This innovative technique could potentially solve one of the most pressing challenges in AI development, including the scalability of training and the critical issue of AI alignment.
The challenge of training large language models
Training and aligning large language models (LLMs) presents several major challenges, which may grow more difficult as models become larger and more capable. Traditionally, LLMs following the popular transformer architecture are trained in two phases:
- Self-supervised learning: The model is fed a vast text corpus and trained…
