Verified claim · AI-ML · 100% confidence
Chinchilla scaling laws introduced in paper: Training Compute-Optimal Large Language Models (Hoffmann et al., 2022).
Last verified 2026-05-16 · Methodology veritas-v0.1 · 8befcae6bce01a95
Structured fields
- Subject
- Chinchilla scaling laws
- Predicate
introduced_in_paper- Object
- Training Compute-Optimal Large Language Models (Hoffmann et al., 2022)
- Confidence
- 100%
- Tags
- chinchilla · scaling-laws · foundational · hoffmann · 2022 · deepmind · nips
Sources (2)
[1] preprint · arXiv (Hoffmann et al., DeepMind) · 2022-03-29
Training Compute-Optimal Large Language Models“We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained.”
[2] peer reviewed · NeurIPS Foundation · 2022-12-06
Training Compute-Optimal Large Language Models (NeurIPS 2022)
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