Large training data shifts which method leads after fine-tuning
A comparison of two pretraining approaches across data sizes; neither dominates. One method wins with big data, the other with small data.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Catherine Li, Jingchu Gai, Andrej Risteski, Jacob Mitchell Springer, Aditi Raghunathan
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers trained language models using two optimizers,AdamW (a way to adjust learning rules during training) and Muon (another training method),and tested them after fine-tuning on several data sets. Muon performed better with small pretraining data, while AdamW did better with large data; the gap shrinks as models grow.
Why it matters
For teams building large AI systems, the choice of pretraining approach depends on how much data is available.
There is a trade-off between initial training loss and how well a model retains its learned knowledge after fine-tuning. It does not claim one method always wins.
Practically, teams should test both options with their own data sizes to see which keeps performance and avoids forgetting the most.
What remains uncertain
Findings depend on the study's data budget and fine-tuning setup; results may vary with different tasks or data mixes.
Original sources · 1
- Pretraining Data Scale Reverses the Muon-AdamW Fine-Tuning Gap ↗arXiv · 2026-11-02
Check the original paper for its authors, methods, version and access terms.
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