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Is letting AI 'think longer' better than building bigger AI models?

For reasoning tasks at fixed total compute, scaling test-time (inference) compute yields larger accuracy gains than spending equivalent compute on increasing model parameters.

From Is letting AI 'think longer' better than building bigger AI models?.

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For reasoning tasks at fixed total compute, scaling test-time (inference) compute yields larger accuracy gains than spending equivalent compute on increasing model parameters. Snell et al. find this holds for easy/medium problems but not the hardest; scope crux is difficulty.

  1. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Snell et al., 2024) (opens in a new tab)Unverified
  2. Scaling Laws for Neural Language Models (Kaplan et al., 2020) (opens in a new tab)Unverified

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