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The pursuit of LLM performance is shifting from raw scale to strategic resource allocation, revealing a critical need to re-evaluate scaling laws for parameter-efficient, compute-optimal training in constrained environments.
Training a small Llama model reveals that relying on final performance metrics can mask critical instabilities and diminishing returns during the learning process.
