Learning-rate scaling in LLMs may be nonlinear
An article published on June 30, 2026, evaluates learning rates in GPT-2-style models ranging from 22 million to 707 million parameters, trained on 5 billion to 100 billion tokens.
Source: A escala da learning rate no treino de LLMs pode ser não linear (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on June 30, 2026, the article evaluates learning-rate scaling in GPT-2-style models ranging from 22 million to 707 million parameters, trained on 5 billion to 100 billion tokens. It reports an upward trend in the optimal learning rate and notes that its scaling may be nonlinear, while also discussing effective learning rate.
For a company training models, a practical response is to record configurations and results for each run and compare experiments at different scales. A monitoring pipeline and dashboard can track the configured rate, effective rate, and training metrics, helping prevent automatic extrapolation from smaller runs.
How the consultancy can help
Wendelmaques can diagnose the training process and available data, define a scope for instrumentation and run comparisons, and implement pipelines, dashboards, and operating routines to track learning rates.
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