Reasoning Models on Programmer.ie: Modern AI programming

Models From First Principles 08: Which Model Should You Use? MR.Q, EBT, SICQL, HRM, Tiny and PACS Compared

Sat, 08 Aug 2026 15:11:00 +0100

This is the final post in Models From First Principles.

The earlier posts asked a sequence of architectural questions:

This post asks the question that matters when building a real system:

Sat, 08 Aug 2026 14:55:00 +0100

The previous post introduced a much more ambitious architecture.

Instead of taking one representation and predicting from it once, the Hierarchical Reasoning Model repeatedly updated two latent states:

input
  ↓
low-level state
  ↓ ↓ ↓
high-level state
  ↓
repeat

That gave us something genuinely new:

computation could continue without adding a new set of parameters for every step.

Sat, 08 Aug 2026 14:48:00 +0100

The previous models in this series were mostly one-pass models.

MR.Q took two embeddings and produced one score.

EBT kept the same basic structure but added several heads.

SICQL made those heads explicit components.

The architecture grew, but the shape of the computation was still familiar:

input
  ↓
encoder
  ↓
representation
  ↓
heads
  ↓
outputs

HRM changes the question.

Sat, 08 Aug 2026 14:27:00 +0100

This is the first post in Models From First Principles.

The previous PyTorch: Zero to Hero series worked from the bottom up.

We started with tensors.

Then gradients.

Then nn.Module.

Then data pipelines, convolution, attention, debugging, performance and finally a small GPT-style language model built from scratch.

That series answered:

What are the pieces?

This series asks a different question:

What happens when we start composing those pieces into increasingly sophisticated models?