> Canonical page: https://posttrainllm.com/inspiration/halo

A note of thanks · Scale boundary

# Halo

Halo brings pre-training, SFT, preference work, and RL into one training framework that spans a GPU and clusters. For this lab it is a map of the single-Mac boundary, not a Mac runtime dependency.

01 · The idea

## What stayed with us

Halo brings pre-training, SFT, preference work, and RL into one training framework that spans a GPU and clusters. For this lab it is a map of the single-Mac boundary, not a Mac runtime dependency.

02 · The local translation

## What we did with it

Halo is a useful architecture reference for the factory loop from pre-training through RL. We are mapping where its single-GPU path ends and its expert, context, tensor, and data parallel paths begin. That gives the Mac lab a precise vocabulary for future scale without changing the current local implementation.

03 · The boundary

## Where the comparison stops

Halo's published training path targets PyTorch and CUDA hardware. We have not run it on this Mac, reproduced its benchmarks, or adopted it as a dependency. Any future use would need a separate target, frozen evaluator, and resource budget.

Source trail

## Follow the work

- Original project [Halo source ↗](https://github.com/whitecircle/halo)
- Our context [Our Mac and cluster boundary map ↗](https://posttrainllm.com/docs/learn/mac-mastery-map/)

Independent appreciation. The named projects have not endorsed or affiliated with PostTrainLLM.