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Compute

Test-time compute is computational effort allocated during inference to process, search, reason about, or adapt to a task before producing an output.

How 2 Manning books explain it

Domain-Specific Small Language Models

The practice of allocating more computation at inference time to improve a model’s output quality or reasoning behavior without changing the base model.

How it is used
Improves difficult math, logic, and coding responses by spending additional inference computation on exploration and evaluation.
How deep it goes
Defines TTC and explains search, candidate scoring, verification, proxy orchestration, and compute-cost tradeoffs.
Examples
Demonstrates OptiLLM decoding with Qwen2.5-0.5B-Instruct on a word problem and GSM8K benchmark samples.
What you can do after
Apply it via OptiLLM to refine responses from OpenAI API–compatible services or local Hugging Face models.

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Sutskever's List

Test-time compute is the allocation of additional computation during inference so a model can perform more extensive internal processing before producing an output.

How it is used
Uses additional inference-time computation to support reasoning as pretraining alone yields diminishing returns on reasoning tasks.
How deep it goes
Explains scratchpads and multi-chain selection, but does not specify how to implement or schedule additional computation.
Examples
Illustrates scratchpad prompting through query subtasks and multi-chain reasoning through search and consistency voting.
What you can do after
Evaluate how additional inference-time deliberation may improve reliability and where prompting falls short on novel constraints.

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Related concepts

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