Multi-Token Prediction and the Dissolution of the Next-Token Horizon
Training frontier language models to forecast multiple future tokens simultaneously breaks greedy local optima, enhancing algorithmic representation learning and enabling native speculative generation.
★ KEY OPERATIONAL TAKEAWAYS ★
- [ANALYSIS] Forecasting multiple subsequent tokens forces shared trunk representations to anticipate long-range algorithmic structure rather than local statistical noise.
- [ANALYSIS] Trained multi-token heads function natively as speculative decoding draft proposals, providing up to three times faster inference without separate draft models.