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IQuest Research Reveals MuonH Optimizer's Advantage Comes from Implicit Learning Rate Scheduling

IQuest Research team found that the advantage of the MuonH optimizer stems primarily from its implicit effective learning rate scheduling, not from a better update direction. By dynamically adjusting the learning rate of a non-Hyperball optimizer to match MuonH's angular effective learning rate, they reproduced its training dynamics. The study also shows that aggressive learning rate decay for MuonH can accelerate early convergence but may harm final performance.

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MuonH Is Not a Free Lunch: IQuest Research Reveals Its Advantage Comes from Implicit Learning Rate Scheduling

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IQuest Research team's experiments show that the advantage of the Hyperball optimizer MuonH comes not from better update directions but from an implicitly formed effective learning rate schedule. The study emphasizes that even with fixed angular velocity, learning rate scheduling remains key.

  • IQuest Research team found that MuonH's advantage mainly stems from implicit effective learning rate scheduling, not better update directions.
  • By dynamically adjusting the learning rate, MuonWD can reproduce MuonH's training dynamics, indicating that the difference mainly comes from effective step size evolution.
  • More aggressive learning rate decay can accelerate early convergence but may harm final performance, highlighting the importance of scheduling strategy.
Open section navigationResearch Background and Core Questions

Research Background and Core Questions

In large-scale pretraining, optimizers are a critical component. Hyperball optimizers like MuonH make explicit the spherical motion that is implicitly formed during training with ordinary optimizers, allowing researchers to more directly control the rate of change of model parameter directions. However, the core practical question is: how should one configure a matching learning rate schedule for MuonH to truly realize its advantages?

A representative phenomenon in Hyperball training is that performance is poor in the early stages but may surpass the baseline in the middle or later stages. This raises two questions: if Hyperball mainly works by suppressing radial updates, why does this phase transition occur? If its main effect is to change the effective step size, then would simply adjusting the learning rate schedule allow non-Hyperball optimizers to exhibit the same behavior?

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This article's information mainly comes from an article submitted by the IQuest Research team to QbitAI, which is a self-report by the research team and has not been independently verified by third parties. Some experimental details and conclusions are claimed by the team and should be treated with caution.

Sources for this version

  1. IQuest给出大规模预训练新视角：早期收敛快不等于最终效果好，调度策略决定MuonH上限量子位

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