Introduces a training strategy where Transformers 'think' in latent space before committing to discrete tokens.
March 24, 2026
Original Paper
Thinking into the Future: Latent Lookahead Training for Transformers
arXiv · 2603.20219
The Takeaway
Breaks the 'one-pass-per-token' constraint of autoregressive models by allowing the network to refine predictions through latent lookahead, solving planning tasks (Sudoku, Mazes) that traditional next-token prediction fails to handle efficiently.
From the abstract
Autoregressive language models trained with next-token prediction generate text by sampling one discrete token at a time. Although very scalable, this objective forces the model to commit at every step, preventing it from exploring or reflecting upon multiple plausible continuations. Furthermore, the compute allocation across tokens is uniform; every token is formed based on a single forward-pass, potentially limiting the model's expressiveness in cases where difficult tokens require inherently