Test-Time Compute
30 min read
What is Test-Time Compute?
Inference-time scaling
What if AI could "think harder" on difficult problems? Test-time compute does exactly this—allowing models to use more computation during inference for better answers, not just during training.
The Inference Scaling Revolution
Traditionally, all the compute went into training. Inference was cheap and fast. But what if harder problems deserve more inference compute? This insight drives techniques like chain-of-thought, self-consistency, and tree of thoughts—all ways of using more computation at query time for better results.
Chain of Thought
Instead of answering directly, the model "shows its work" step by step. For math: "First, I'll calculate X. Then I'll apply that to Y. Therefore, the answer is Z." This simple technique dramatically improves accuracy on reasoning tasks. The extra tokens are extra compute being used productively.
When to Use More Compute
Simple queries don't need extended reasoning—they'd just waste time and money. Complex queries benefit from more compute. The art of test-time compute is knowing when to apply it and how much. Production systems might route simple queries to fast paths and complex ones to extended reasoning.
💡 Key Takeaways
- Test-time compute = more reasoning during inference
- Chain-of-thought dramatically improves reasoning accuracy
- Match compute to problem difficulty
- The future of AI includes dynamic compute allocation
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