# Test-Time Compute Scaling

> Spend more compute at inference time to get better answers — not just bigger models.

- **Category**: Agentic AI
- **Subcategory**: Reasoning
- **Canonical URL**: https://designpattern.fyi/patterns/test_time_compute_scaling/

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## Description
**Intent**: Trade inference compute for answer quality. At test time, generate more candidates, search deeper, or reason longer to improve the output without changing model weights.

**Context**: Scaling model size has diminishing returns and high training costs. Scaling inference compute is more flexible — you can dial it up or down per request, pay for it per call, and apply it selectively to hard problems. OpenAI's o1/o3, Google's Gemini thinking, and Anthropic's extended thinking are all commercial implementations of this idea.

**Solution**: Concrete implementations: (1) Best-of-N sampling — generate N answers, pick the best via a verifier or reward model. (2) Extended thinking / reasoning tokens — give the model more steps to think before answering. (3) Tree/graph search — explore multiple reasoning paths, prune, return the best. (4) Iterative refinement — generate, critique, revise, repeat. Scale compute up for hard tasks, down for easy ones. See also: adaptive-compute-allocation, extended-thinking, best-of-n-sampling, tree-of-thoughts.



## Use Cases
- Hard tasks where a single generation is unreliable and retrying is cheap.
- Any domain where correctness is verifiable and worth paying extra compute for.
- Agentic loops where the quality of a planning step multiplies through subsequent actions.






## Trade-offs


### Advantages

- Quality improvement without retraining — works on any existing model.

- Granular control — spend compute exactly where it's needed.

- Parallelizable strategies (best-of-N) can run in the same wall-clock time.




### Considerations & Drawbacks

- Compute cost scales up fast — best-of-N at N=32 is 32x the token cost.

- Requires a verifier or reward model to select among candidates — adds system complexity.

- Latency increases are significant for sequential strategies (chain refinement, deep search).







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/test-time-compute-scaling/)