NITP: Next Implicit Token Prediction for LLM Pre-training

Abstract

Standard Next-Token Prediction (NTP) supervises language models solely through discrete labels in the output logit space. We argue that this sparse, one-hot supervision leaves the latent representation space under-constrained, allowing hidden states to drift into degenerate and anisotropic configurations that limit generalization. To address this issue, we propose **Next Implicit Token Prediction (NITP)**, which augments discrete prediction with dense, continuous supervision directly in the representation space. NITP requires the model to predict the implicit semantic content of the next token, using shallow-layer representations from the same model as stable self-supervised targets. Theoretically, we show that NITP regularizes the optimization landscape by eliminating under-constrained degrees of freedom and enforcing a compact, structured representation geometry. Empirically, across dense and MoE models ranging from 0.5B to 9B parameters, NITP consistently **improves downstream performance with negligible computational overhead**. Notably, on the 9B MoE model, NITP achieves a **5.7%** absolute improvement on MMLU-Pro, along with gains of **6.4%** on C3 and **4.3%** on CommonsenseQA, with **~2%** additional training FLOPs and no additional inference cost.