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.