GPT-4.5: "Not a frontier model"?

OpenAI's GPT-4.5 release marks a significant scaling milestone with improved capabilities in reduced hallucinations and emotional intelligence, though its impact is less dramatic than previous iterations. Despite being OpenAI's largest publicly available model, its high computational requirements and pricing raise questions about the practical value versus existing solutions. The model's true significance may lie in its potential integration with future AI developments rather than standalone chat capabilities.

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

A novel language model architecture enables scaling test-time computation through latent space reasoning using a recurrent block approach, achieving performance improvements equivalent to 50B parameters without specialized training data or large context windows.

How to Scale Your Model

A comprehensive guide explaining how to optimize and scale Large Language Models (LLMs) on TPU systems, covering everything from hardware architecture to practical implementation in JAX. The book breaks down complex topics like model parallelism, training efficiency, and inference optimization, making it valuable for both researchers designing architectures and engineers focused on performance.

Model Scaling

GPT-4.5: "Not a frontier model"?

Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

How to Scale Your Model