
During March 2026, Yizhe Zhang developed and documented a reproducible benchmarking experiment in the marin-community/marin repository, focusing on optimizer performance for large-scale models. He set up an end-to-end experiment comparing the Muon optimizer to Adam on a 50-million parameter Llama model at 1× Chinchilla scale, providing detailed configurations and expected results to guide future data-driven decisions. Using Python, data analysis, and machine learning techniques, Yizhe established a baseline and configuration scaffold that enables consistent evaluation across runs. The work demonstrated depth in experimental design and reproducibility, laying a foundation for ongoing optimization strategy research within the project.
Month: 2026-03; Focused on delivering a measurable, business-value oriented experiment to benchmark optimization strategies for large-scale models in marin-community/marin. Key work this month centered on setting up and documenting an end-to-end experiment comparing Muon optimizer against Adam on a 50M-parameter Llama model at 1× Chinchilla scale, with configurations and expected results to guide future data runs. The work was committed and prepared for reproducibility across runs. Concise outcomes: - Established a baseline and configuration scaffold for Muon vs Adam benchmarking on a representative 50M Llama setup. - Delivered reproducible experiment setup and results blueprint to accelerate data-driven optimizer decisions. - Implemented and submitted the speedrun experiment llama_50m_muon_1x - Muon optimizer at 1× Chinchilla scale (commit: 0d342a5bdf887dd69a2a3e8bbcf5ba690e23b9a6).
Month: 2026-03; Focused on delivering a measurable, business-value oriented experiment to benchmark optimization strategies for large-scale models in marin-community/marin. Key work this month centered on setting up and documenting an end-to-end experiment comparing Muon optimizer against Adam on a 50M-parameter Llama model at 1× Chinchilla scale, with configurations and expected results to guide future data runs. The work was committed and prepared for reproducibility across runs. Concise outcomes: - Established a baseline and configuration scaffold for Muon vs Adam benchmarking on a representative 50M Llama setup. - Delivered reproducible experiment setup and results blueprint to accelerate data-driven optimizer decisions. - Implemented and submitted the speedrun experiment llama_50m_muon_1x - Muon optimizer at 1× Chinchilla scale (commit: 0d342a5bdf887dd69a2a3e8bbcf5ba690e23b9a6).

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