🤖 AI Summary
This work addresses the high computational cost and low data throughput efficiency of large language model pretraining, which often necessitates modifications to system architecture in existing approaches. The authors propose Token-Superposition Training (TST), a two-stage pretraining method that requires no changes to parallelization strategies, optimizers, tokenizers, data pipelines, or model architectures. In the first stage, consecutive tokens are compressed into bags and trained efficiently using multi-hot cross-entropy loss with superposition; standard training is then resumed in the second stage. Evaluated across models ranging from 270M to 10B parameters, TST significantly improves data throughput per FLOP, reduces pretraining time by up to 2.5× at equivalent loss, and consistently outperforms baseline methods on downstream tasks, demonstrating strong generality and plug-and-play compatibility.
📝 Abstract
Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in order to achieve high data throughput. In this work, we present Token-Superposition Training (TST), a simple drop-in method that significantly improves the data throughput per FLOPs during pre-training without modifying the parallelism, optimizer, tokenizer, data, or model architecture. TST is done in two phases: (i) A highly efficient superposition phase where we combine many contiguous tokens into one bag and train using a multi-hot cross-entropy (MCE) objective, and (ii) a recovery phase where we revert back to standard training. We extensively evaluate TST on the scale of 270M and 600M parameters and validate on 3B and a 10B A1B mixture of experts model, demonstrating that it is highly robust in different settings. Ultimately, TST consistently outperforms baseline loss and downstream evaluations, and under equal-loss settings, TST yields up to a 2.5x reduction in total pre-training time at the 10B A1B scale.