Warm-starting encoder-decoder models with pre-trained checkpoints (BERT, GPT-2) in Transformers

AI Impact Summary

The document describes warm-starting encoder-decoder models by initializing the encoder and/or decoder from pre-trained checkpoints (e.g., BERT, GPT-2) to approximate large seq2seq models like T5 or Pegasus at a fraction of the training cost. This approach, demonstrated in Rothe et al. 2020 and implemented via the EncoderDecoderModel framework in 🤗Transformers, enables faster prototyping and broader access to high-quality sequence generation without full pre-training. Teams should plan compatibility considerations and benchmarking to ensure task-specific gains hold across translation, summarization, and rephrasing tasks while migrating from traditional end-to-end pre-training pipelines.