3 papers
cs.LG2026
Scaling Down the Scaling Laws: Parameter Efficiency and Compute-Optimal Training in Resource-Constrained Large Language Models
Joe Dwyer
Large language models (LLMs) have achieved substantial performance gains through increases in model size, training data, and computational resources. However, traditional scaling a…
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…