Learning the Latent Rules of a Game from Data: A Chess Story
Authors: name: Ben Fauber

We demonstrate that small pretrained foundational generative language models
with millions of parameters can learn the latent rules of a process from data
associated with the process. Inspired by Stefan Zweig's novella
"Schachnovelle," also known as "The Royal Game" in English, we show that 28M
and 125M parameter pretrained foundational small language models (SLMs) can be
instruction fine-tuned with 1,000-to-1,000,000 examples to learn the rules of
chess, propose legal moves, and accurately solve chess problems. We also
explore the impact of successive language model fine-tuning epochs on improved
outcomes and demonstrate reductions in model hallucinations by increasing the
number of instruction fine-tuning examples.