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Analysis of LLM Knowledge Cutoffs and Pre-Training Timelines
Key points
- Probing models with niche facts estimates parameter counts.
- Measuring token breakdown reveals data mixture insights.
- Date-related questions estimate training timelines.
- LLM training involves pre-training, domain-specific data, and assistant persona refinement.
Inferring LLM Training Details
Researchers can deduce hidden facts about how frontier large language models (LLMs) are trained by using carefully designed requests. This method involves scoring models on niche facts to approximate parameter counts and analyzing how models break down tokens to infer data mixture compositions. Additionally, asking date or self-identification related questions helps estimate training timelines.
Three Stages of LLM Training
The training of large language models typically converges into three stages. First, a massive amount of general-purpose data, often scraped from the internet, is used to pre-train a large auto-complete model. Second, domain-specific, high-quality data improves the base models and extends capabilities like long-text understanding. Finally, the base model is refined into an 'assistant' persona, focusing on personality, reasoning, and tool-calling abilities.
Pre-training Checkpoints and Model Versions
Generating the initial pre-training checkpoint is one of the most expensive and data-intensive steps in LLM development. Pre-training teams often conduct multi-month runs to create these base checkpoints, which frequently correspond to major versions of released models (e.g., GPT-4 to GPT-5). Concurrently, capability and post-training teams experiment to improve the most recent base models, leading to minor version releases or distilled smaller variants.
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Reporting from
An analysis explores methods to infer the training timelines and data mixtures of large language models like Claude and GPT by probing their knowledge cutoffs and tokenization. This approach helps estimate how these frontier models are developed and what data they were trained on, despite limited public information.