Token Superposition Training

Nous Research's second pretraining paper of the week. Token Superposition Training (TST) is a modification to the standard LLM pretraining loop that produces a 2 to 3x wall-clock speedup at matched FLOPs without changing the model architecture, optimizer, tokenizer, or training data. During the first third of training, the model reads and predicts contiguous bags of tokens, averaging their embeddings on the input side and predicting the next bag with a modified cross-entropy on the output side. For the remainder of the run, training reverts to standard next-token prediction. The inference-time model is identical to one produced by conventional pretraining. TST was validated at 270M, 600M, and 3B dense scales, and at a 10B-A1B mixture-of-experts model where it reaches a lower final loss while consuming 4,768 B200-GPU-hours versus the baseline's 12,311. Together with Lighthouse Attention, this is the second pretraining-loop modification from the same lab this week reporting substantial speedups without architecture changes.