Hyperparameter Transfer beyond MuP

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#3058, #3715 introduces MuP into Megatron-LM with support for Muon.
MuP allows efficient and reliable hyperparameter (esp. LR) transfer from narrow to wide networks, for the same depth.

There are a series of papers in this field, that allow transfer from shallow to deeper networks, etc, which are essential for a good pretraining scaling recipe.

A new paper from Microsoft, HyperP, claims to be "the first framework for transferring optimal learning rates across model width, depth, training tokens, and Mixture-of-Experts (MoE) granularity under the Frobenius-sphere constraint with the Muon optimizer."

I suggest we integrate HyperP into Megatron-LM.
Link: [Rethinking Language Model Scaling under Transferable Hypersphere Optimization](https://arxiv.org/pdf/2603.28743), Ren et al, 2026.

Tag the [@mcore-oncall](https://github.com/orgs/NVIDIA/teams/mcore-oncall)
to get oncall's attention to this issue.

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