Latent Reasoning

Reasoning is expensive mostly because we make the model say it. That sounds almost too simple, which is usually where trouble begins. Chain-of-thought reasoning improved language-model performance by giving the model a written workspace: first solve, then answer. But the same trick also turns internal computation into external communication. Every intermediate step must be decoded, formatted, and passed forward one token at a time. The model is not just thinking; it is producing a small essay it may not need to show anyone. ...

Sudoku is a useful toy problem because it is cruel in exactly the right way. A nearly completed grid with one blank cell should be easier than a brutal puzzle with dozens of missing entries. Humans know this. Basic software knows this. A model that can solve hard Sudoku should not suddenly collapse when the puzzle becomes almost finished. ...

TL;DR for operators SynAdapt is not a paper about making models “think secretly” because mystery sells better on conference posters. It is a paper about inference budgeting: when a model should spend tokens explaining its reasoning, and when it can compress that reasoning into latent vectors and move on. The method trains a large language model to use synthetic continuous chain-of-thought—CCoT—as a dense internal reasoning representation instead of generating long natural-language reasoning traces. For easier problems, the model answers using this latent representation directly. For harder problems, a difficulty classifier detects that silent reasoning is likely insufficient and routes the question back to discrete chain-of-thought, with a prompt that keeps the re-thinking concise.1 ...