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Test-Time Compute

Using additional computation during inference (not training) to improve AI model outputs.

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Definition

Test-time compute refers to allocating more computational resources when the model is generating outputs, rather than only investing compute during training.

Key Insight: Instead of just making models bigger, you can make them "think longer" on hard problems.

Techniques: - Chain of thought reasoning - Multiple sampling and voting - Tree search over responses - Self-verification - Extended thinking time

OpenAI o1 Approach: - Model generates internal reasoning - More tokens for complex problems - Self-checks before responding - Trade-off: slower but more accurate

When It Helps: - Math problems - Coding challenges - Complex reasoning - Multi-step logic

Trade-offs: - Longer response times - Higher inference costs - May not help simple tasks

Examples

OpenAI o1 spending more "thinking tokens" on a hard math problem to ensure the answer is correct.

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