Enhancing next token prediction based pre-training for jet foundation models

Abstract Next token prediction (NTP) is an attractive pre-training task for jet foundation models, in that it is simulation free and enables excellent generative capabilities that can transfer across datasets. Here we study multiple improvements to NTP, building on the initial work of OmniJet- α . Instead of tokenizing particles and subsequently only using the token-IDs as the model input for both the generative and the classification task, we adopt a hybrid setup, which allows us to use continuous feature vectors as model input while only using token-IDs in the NTP target. Secondly, we explore a combined pre-training strategy that combines masked particle modeling and generative learning objectives. Taken together, these changes greatly improve the performance in downstream classification tasks without any loss in generative performance.