---
id: "kb-2026-00001"
title: "Large Language Model Training: Scaling Laws, Data Curation, and Compute"
schema_type: "TechArticle"
category: "ai"
language: "en"
confidence: "medium"
last_verified: "2026-06-01"
created_date: "2026-05-26"
generation_method: "human_only"
derived_from_human_seed: true
conflict_of_interest: "none_declared"
is_live_document: false
data_period: "static"
atomic_facts:
  - id: "af-llm-training-1"
    statement: "Kaplan et al. report empirical scaling laws in which language-model loss scales predictably with model size, dataset size, and training compute."
    source_title: "Scaling Laws for Neural Language Models"
    source_url: "https://arxiv.org/abs/2001.08361"
    confidence: "medium"
  - id: "af-llm-training-2"
    statement: "Kaplan et al. argue that compute allocation can be optimized by balancing model size, data, and training time."
    source_title: "Scaling Laws for Neural Language Models"
    source_url: "https://arxiv.org/abs/2001.08361"
    confidence: "medium"
  - id: "af-llm-training-3"
    statement: "Hoffmann et al. find that compute-optimal transformer training should scale model size and training-token count together."
    source_title: "Training Compute-Optimal Large Language Models"
    source_url: "https://arxiv.org/abs/2203.15556"
    confidence: "medium"
  - id: "af-llm-training-4"
    statement: "The Chinchilla study reports that a 70B parameter model trained with substantially more data outperformed larger undertrained models across many downstream evaluations."
    source_title: "Training Compute-Optimal Large Language Models"
    source_url: "https://arxiv.org/abs/2203.15556"
    confidence: "medium"
  - id: "af-llm-training-5"
    statement: "The GPT-3 paper presents in-context learning as evaluating a pretrained language model on tasks described through prompts and examples, without gradient updates at evaluation time."
    source_title: "Language Models are Few-Shot Learners"
    source_url: "https://arxiv.org/abs/2005.14165"
    confidence: "medium"
completeness: 0.84
known_gaps:
  - This article covers stable training concepts, not current vendor model recipes or private dataset mixtures.
  - It does not prescribe a training budget or claim that scaling alone guarantees agent reliability.
disputed_statements: []
primary_sources:
  - id: ps-llm-training-1
    title: "Scaling Laws for Neural Language Models"
    type: "academic_paper"
    year: 2020
    institution: "arXiv"
    url: "https://arxiv.org/abs/2001.08361"
  - id: ps-llm-training-2
    title: "Training Compute-Optimal Large Language Models"
    type: "academic_paper"
    year: 2022
    institution: "arXiv"
    url: "https://arxiv.org/abs/2203.15556"
  - id: ps-llm-training-3
    title: "Language Models are Few-Shot Learners"
    type: "academic_paper"
    year: 2020
    institution: "arXiv"
    url: "https://arxiv.org/abs/2005.14165"
secondary_sources: []
updated: "2026-06-01"
---

## TL;DR

Large language model training is a resource-allocation problem across model size, token count, data quality, and compute. For AI programming agents, the practical lesson is that model capability depends on training choices, but task reliability still needs retrieval, tools, tests, and evals.

## Core Explanation

Scaling-law research makes one stable point: language-model training is not just "make the model bigger." Loss and downstream behavior depend on how compute is split across parameters, tokens, and optimization. Later compute-optimal work sharpened that point by showing that some large models were undertrained relative to the amount of available compute.

This matters for agent builders because the training story and the runtime story are separate. A stronger base model can improve reasoning, code synthesis, and tool-use fluency, but an agent still needs explicit context selection, source grounding, permission boundaries, and regression tests.

## Detailed Analysis

Use this article when deciding what claims an AI agent can safely make about LLM training. The source-backed claims are about scaling laws, compute-optimal token/model allocation, and prompt-based in-context evaluation. They do not prove that a particular commercial model is suitable for a specific coding, game-production, or video-generation workflow.

For production planning, agents should separate:

- pretraining claims: model size, tokens, data curation, and compute;
- adaptation claims: fine-tuning, LoRA, RLHF, or instruction tuning;
- runtime claims: retrieval, tool calls, tests, and human review;
- evaluation claims: benchmarks that match the target task.

## Further Reading

- [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361)
- [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556)
- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)

## Related Articles

- [AI Training Data Curation](/ai/ai-training-data-curation/)
- [Distributed Training Systems](/ai/distributed-training-systems/)
- [Test-Time Compute Scaling](/ai/test-time-compute-scaling/)