Mixture of Experts – How Modern LLMs Scale Without Proportional Cost
A useful mental model for thinking about large language models is that they are very large lookup tables. During training, they compress an enormous amount of information about…
RLHF Explained – How Language Models Learn to Follow Instructions
If you used an early GPT model – the kind available before 2022 – and asked it to explain something clearly, it would often respond by continuing your…
The Vanishing Gradient Problem – Why Deep Networks Forget Where They Came From
There is a paradox buried in the design of deep neural networks. The deeper you make a network, the more expressive it should become – more layers means…
Diffusion Models Explained – From Noisy Images to State-of-the-Art Generation
For most of the 2010s, if you wanted to generate photorealistic images with a neural network, you worked with GANs – Generative Adversarial Networks. The results were impressive.…
Why Your Model Hallucinates – And What Researchers Are Actually Doing About It
If you’ve used a large language model for anything factual – a date, a citation, a person’s biography – you’ve probably encountered it: the model answers with complete…
Attention Is All You Need – But Do We Understand Why?
In 2017, a Google Brain paper with a quietly confident title rewired how the world thinks about sequence modeling. Attention Is All You Need didn’t just introduce a…