Scaling for Reasoning: Do Think Tokens Change the Law for LLMs?
Discover how 'think tokens' and test-time scaling challenge traditional LLM scaling laws. Learn why letting models reason longer beats making them bigger for complex tasks.
How Training Duration and Token Counts Affect LLM Generalization
Explore how training duration and token counts impact LLM generalization. Learn why variable sequence lengths beat fixed chunks and how to avoid the generalization valley.
Data-Centric vs Model-Centric Scaling: The Real Path to Better LLMs
Explore the shift from model-centric to data-centric scaling for LLMs. Learn how data quality, compression, and governance drive better AI performance and efficiency in 2026.
Hyperparameters That Matter Most in Large Language Model Pretraining
Learn which hyperparameters matter most in LLM pretraining: learning rate and batch size. Discover the Step Law formula that predicts optimal settings using model size and dataset size, saving time and improving performance.
