arXiv:2605.24956v1 Announce Type: new 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 can 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 trains the model to predict the
The paper leverages current understandings of large language model limitations, specifically regarding latent representation spaces, to propose a new pre-training method.
Improving LLM pre-training efficiency and performance can accelerate AI development, potentially leading to more capable and less resource-intensive models.
This new method could lead to more robust and generalizable AI models by addressing fundamental issues in their internal representations, impacting future AI architecture design.
- · AI researchers
- · LLM developers
- · Cloud computing providers
- · AI-reliant industries
- · Companies with less sophisticated LLM pre-training techniques
Increased efficiency and performance of future large language models.
Reduced computational cost for training highly capable AI models, broadening access to advanced AI.
Acceleration in the development of sophisticated AI agents and autonomous systems across various sectors.
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