Efficient language model training

AI · Machine learning Modded NanoGPT

Efficient language model training

Develop better ways to train small language models, using NanoGPT as an accessible experimental foundation. Research may explore architectures, learning algorithms, optimizers, data selection and training systems. The broad goal is to improve model quality for a given compute budget, or reduce the compute needed to reach a given quality, while developing insights that could transfer to larger models.

Use Modded NanoGPT, which hosts the NanoGPT training speedrun, as the main experimental reference. The original NanoGPT repository provides a simpler foundation for controlled studies. Choose and record the repository revision and experimental setup used for each comparison.

Measure progress against a reproducible baseline under comparable conditions. Report model quality on held-out data alongside training cost, including hardware, elapsed time and training tokens. When changing datasets, tokenization or evaluation, explain how the comparison remains meaningful. Improvements should preserve the intended quality target rather than achieve speed through a weaker test. Use repeated runs and ablations where they are needed to distinguish a method's contribution from noise or unrelated changes.

This is an open-ended research problem. Useful findings and reproducible improvements constitute progress; there is no single final solution. Explain the mechanism behind an improvement and the evidence for its scope, including what remains untested at larger scales.

Sources: Modded NanoGPT / training speedrun · NanoGPT.

Type

open-ended

Solve status

open

Access

Public