Can a New Training Objective Make LLMs See Further and Reason Better?

The paper introduces Next‑ToBE, a training‑objective modification that replaces the one‑hot next‑token label with a soft distribution covering a future token window, thereby activating latent anticipatory capacity in large language models and yielding significant gains in token‑hit rates, reasoning accuracy, and training efficiency.

Anticipatory CapacityNext-ToBEToken Prediction

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Can a New Training Objective Make LLMs See Further and Reason Better?

Mike Chen's Internet Architecture

May 21, 2026 · Artificial Intelligence

Demystifying AI Large Models: Architecture, Principles, and Workflow

The article explains that large language models are massive probability engines built on the Transformer architecture with self‑attention, trained through costly pre‑training on trillions of tokens, then refined by instruction fine‑tuning and RLHF, ultimately predicting the next token to generate text.

Large Language ModelRLHFSelf-Attention

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Demystifying AI Large Models: Architecture, Principles, and Workflow