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Test-Time Compute Scaling(TTC)
Dynamically allocates computational resources based on problem complexity
In 30 seconds
- What
- Allocates more reasoning steps or processing power to harder problems, fewer to simpler ones, adjusting in real time based on detected difficulty.
- When to use
- Problems vary widely in complexity and you can afford variable latency, or when some queries need deep reasoning while others need quick answers.
- Watch out
- Estimating problem complexity upfront is unreliable; you may overspend on easy problems or underspend on hard ones before detecting the mismatch.
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Test-Time Compute Scaling: Overview
Dynamically allocates computational resources based on problem complexity
- Dynamic compute allocation
- Complexity-aware scaling
- Adaptive thinking time
- Resource optimization
- Quality-compute trade-offs
- Real-time adjustment
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References
The papers, specifications, and repositories this pattern is based on.
- Scaling Laws for Neural Language Models (Kaplan et al., 2020)arXiv:2001.08361
- Training Compute-Optimal Large Language Models (Hoffmann et al., 2022)arXiv:2203.15556
- Let's Verify Step by Step (Lightman et al., 2023)arXiv:2305.20050
- STaR: Bootstrapping Reasoning With Reasoning (Zelikman et al., 2022)arXiv:2203.14465
- OpenAI Latency Optimization Guide
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