# RoBERTa: A Robustly Optimized BERT Pretraining Approach

## TL;DR

This paper introduces RoBERTa, an optimized BERT pretraining method that improves performance by carefully tuning hyperparameters and training data, achieving state-of-the-art results on multiple benchmarks.

## Contribution

It provides a comprehensive analysis of BERT pretraining, demonstrating that with proper hyperparameter tuning and training data, BERT can outperform many subsequent models.

## Key findings

- BERT was significantly undertrained in original form
- Proper hyperparameter tuning improves model performance
- RoBERTa achieves state-of-the-art results on GLUE, RACE, and SQuAD

## Abstract

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 carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.