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Test-Time Compute: Scaling Intelligence at Inference

Traditionally, we scale AI by increasing model size or training data. Test-Time Compute scaling suggests that we can also improve performance by allowing the model to spend more “compute” during the inference phase.

Methods of Scaling at Inference

  1. Chain-of-Thought (CoT): Forcing the model to generate intermediate reasoning steps before reaching a final answer.
  2. Best-of-N Sampling: Generating multiple candidate answers and using a separate “reward model” or “verifier” to select the best one.
  3. Iterative Refinement: Allowing the model to critique and revise its own output multiple times before presenting it to the user.

Why It Matters

This shift allows smaller, more efficient models to punch above their weight class on difficult reasoning tasks, making high-level intelligence more accessible without needing trillion-parameter architectures.