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Scaling Laws Across LLM Development Stages
While scaling laws are fundamentally known for guiding pre-training by demonstrating that increased training data, model size, and compute lead to better performance, these predictable principles also apply to downstream stages. Specifically, scaling laws extend to fine-tuning and inference, indicating that performance improvements can be systematically achieved across the entire lifecycle of a Large Language Model.
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Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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A research team is training a large language model and has a fixed, non-negotiable computational budget. Their goal is to achieve the lowest possible final loss. Based on the established principles that govern the relationship between computation, model size, data size, and performance, which of the following strategies represents the most efficient use of their budget?
Evaluating an LLM Training Strategy
Analyzing Deviations from LLM Scaling Behavior
Continued Effectiveness of Scaling up Training in NLP
Power-Law Curve of Performance Scaling
Scaling Laws Across LLM Development Stages