UltraBERT: Beyond the Limit of BERT Pre-Training
Recent bidirectional encoders have been modernized with innovations from causal large language models (LLMs). However, the pretraining recipe itself remains inherited from small-scale conventions, leaving encoders underoptimized, with no single model performing competitively across both classification and retrieval tasks. In this work, we investigate how far encoder performance can be pushed by revisiting core design choices in training, revealing three key findings: (1) Encoders saturate on downstream tasks beyond a few trillion tokens, indicating that additional compute past this point does not help. (2) Across model sizes, lower masking ratios benefit classification while higher ones b...
| Authors | Jungwoo Park, Taewhoo Lee, Jaewoo Kang |
|---|---|
| Primary category | cs.CL |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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