Inference-Time Scaling
The finding that answers keep improving the more compute you let a model spend while answering.
Think of it like
Like turning up a car’s power by flooring the accelerator in the moment, rather than rebuilding the engine.
Example
Charts showing accuracy climbing as a model is allowed to generate longer and longer reasoning are inference-time scaling curves.
How it actually works
It’s the answer-time cousin of training scaling laws: instead of “bigger model, better model,” it’s “more thinking per question, better answer.” This reframed how labs chase performance, since a smaller model with generous inference budget can rival a bigger one on some tasks. The catch is that every extra bit of thinking costs money and time on every single query.
For product teams
Means a modest model can punch above its weight if you’re willing to pay per answer.
For engineers
The empirical scaling of task performance with inference-time compute budget.
Related
- Test-Time Compute — The mechanism it names.
- Scaling laws — The training-time counterpart.
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