Multi-Token Prediction: How Future Token Supervision Densifies Representations and Speeds Up LLM Serving
Standard autoregressive language models are trained under a strict next-token prediction objective. At every sequence position, the model consumes a prefix of tokens and predicts the single immediate successor token using a cross-entropy loss. While this paradigm has scaled language modeling across orders of magnitude, it suffers from an architectural limitation: myopic optimization. By evaluating loss exclusively on the immediate next step, standard training fails to reward representations that
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