Latent Lookahead: training models to plan several steps ahead in hidden space
burkov · x · 2026-11-14
Standard LLMs generate one token at a time, committing to each choice before seeing its downstream effects—a weakness for planning tasks. The article introduces latent lookahead, a training and generation method that lets a model extend its hidden representations several steps into the future.
- Internal predictions are trained against the actual upcoming sequence, and the model can revise earlier predictions using later ones before emitting the next visible token.
- This lets the model spend extra compute on hard choices without first committing to visible output.
- Experiments focus on small models trained from scratch (text truncated).
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