RoBERTa: A Robustly Optimized BERT Pretraining Approach

May 9, 2026

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that …

https://arxiv.org/abs/1907.11692

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[215] => roberta: a robustly optimized bert pretraining aoproach [216] => roberta: a robustly optimized bert pretraining alproach [217] => roberta: a robustly optimized bert pretraining a-proach [218] => roberta: a robustly optimized bert pretraining a0proach [219] => roberta: a robustly optimized bert pretraining aporoach [220] => roberta: a robustly optimized bert pretraining aplroach [221] => roberta: a robustly optimized bert pretraining ap-roach [222] => roberta: a robustly optimized bert pretraining ap0roach [223] => roberta: a robustly optimized bert pretraining appeoach [224] => roberta: a robustly optimized bert pretraining appdoach [225] => roberta: a robustly optimized bert pretraining appfoach [226] => roberta: a robustly optimized bert pretraining apptoach [227] => roberta: a robustly optimized bert pretraining app5oach [228] => roberta: a robustly optimized bert pretraining app4oach [229] => roberta: a robustly optimized bert pretraining appriach [230] => roberta: a 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pretraining approacn [246] => roberta: a robustly optimized bert pretraining approacj [247] => roberta: a robustly optimized bert pretraining approacu [248] => roberta: a robustly optimized bert pretraining approacy ) </h1> </body>