2 papers
cs.LG2026
An Empirical Study on Noisy Data and LLM Pretraining Loss Divergence
Qizhen Zhang, Ankush Garg, Jakob Foerster +3
Large-scale pretraining datasets drive the success of large language models (LLMs). However, these web-scale corpora inevitably contain large amounts of noisy data due to unregulat…
cs.CL2025
BTS: Harmonizing Specialized Experts into a Generalist LLM
Qizhen Zhang, Prajjwal Bhargava, Chloe Bi +9
We present Branch-Train-Stitch (BTS), an efficient and flexible training algorithm for combining independently trained large language model (LLM) experts into a single, capable gen…