Preprint
Aug 2026
TailSFT: Filtered Fine-Tuning Improves Post-Training Performance
A simple modification to supervised fine-tuning, TailSFT, which filters out already fit sequences during training, thereby focusing learning on under-modeled regions, or the tail, of the data distribution, and introduces a lightweight diagnostic for identifying settings where TailSFT is most likely to help.
Sadhika Malladi, Samy Jelassi, Dylan J. Foster et al.
· 2 citations