Learning the Latent Rules of a Game from Data: A Chess Story
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.
66ff3f5c1e307d098692d137
Ben Fauber
claimed_verified
2024-10-04T01:05:37.180Z
6331bab56eae0bb0a01968ea
/avatars/20848394b32d9f2d0037ccfd1050e7bb.svg
BF
user
BFauber
Learning the Latent Rules of a Game from Data: A Chess Story
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.
Pretrained small language models can be fine-tuned to learn and propose legal chess moves through instruction fine-tuning with varying numbers of examples.
pretrained foundational generative language models SLMs instruction fine-tuning model hallucinations