2 papers
cs.AI2026
A Quantitative Experimental Repeated Measures Study of Training Dynamics in a Small Llama Style Language Model Under a Compute-Aware Token Budget
Joe Dwyer
This study examines training dynamics in a small Llama-style language model trained under a fixed, compute-constrained token budget. Rather than evaluating efficiency solely throug…
cs.LG2026
Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency
Joe Dwyer
Research in machine learning has questioned whether increases in training token counts reliably produce proportional performance gains in large language models. Building on prior w…