Multi-Token Prediction Needs Registers
Authors: name: Anastasios Gerontopoulos, name: Spyros Gidaris, name: Nikos Komodakis

Multi-token prediction has emerged as a promising objective for improving
language model pretraining, but its benefits have not consistently generalized
to other settings such as fine-tuning. In this paper, we propose MuToR, a
simple and effective approach to multi-token prediction that interleaves
learnable register tokens into the input sequence, each tasked with predicting
future targets. Compared to existing methods, MuToR offers several key
advantages: it introduces only a negligible number of additional parameters,
requires no architectural changes--ensuring compatibility with off-the-shelf
pretrained language models--and remains aligned with the next-token pretraining
objective, making it especially well-suited for supervised fine-tuning.
Moreover, it naturally supports scalable prediction horizons. We demonstrate
the effectiveness and versatility of MuToR across a range of use cases,
including supervised fine-tuning, parameter-efficient fine-tuning (PEFT), and
pretraining, on challenging generative tasks in both language and vision
domains. Our code will be available at: https://github.com/nasosger/MuToR.