13 citations · 36 across the 11 of their papers we have counts for
5 papers · 1 filter
Data movement limits to frontier model training
Ege Erdil, David Schneider-Joseph
We present a theoretical model of distributed training, and use it to analyze how far dense and sparse training runs can be scaled. Under our baseline assumptions, given a three mo…
FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI
Elliot Glazer, Ege Erdil, Tamay Besiroglu +21
We introduce FrontierMath, a benchmark of hundreds of original, exceptionally challenging mathematics problems crafted and vetted by expert mathematicians. The questions cover most…
Estimating Idea Production: A Methodological Survey
Ege Erdil, Tamay Besiroglu, Anson Ho
Accurately modeling the production of new ideas is crucial for innovation theory and endogenous growth models. This paper provides a comprehensive methodological survey of strategi…
Chinchilla Scaling: A replication attempt
Tamay Besiroglu, Ege Erdil, Matthew Barnett +1
Hoffmann et al. (2022) propose three methods for estimating a compute-optimal scaling law. We attempt to replicate their third estimation procedure, which involves fitting a parame…
Algorithmic progress in language models
Anson Ho, Tamay Besiroglu, Ege Erdil +6
We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning. Using a dataset of over 200 language model evaluations…