The paper 'Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters' explores the use of increased inference-time computation in large language models (LLMs) to enhance their performance, especially on challenging prompts. The authors propose a 'compute-optimal' strategy that allocates test-time compute adaptively based on the difficulty of the prompt, which they categorize into different levels of complexity.

Key findings from the study include that using additional test-time computation can improve LLM outputs more efficiently than merely increasing model size through pretraining. The authors document significant improvements when applying this compute-optimal strategy, stating, 'we can improve the efficiency of test-time compute scaling by more than 4× compared to a best-of-N baseline' [1].

The paper further analyzes two main approaches for scaling test-time compute: (1) searching against dense, process-based verifier models and (2) adaptively updating the response distribution at test time. The effectiveness of these methods varies depending on prompt difficulty, indicating that for easier problems, iterative refinements are typically more beneficial, while harder problems may benefit from broader searches or reconsiderations of potential responses [1].

Moreover, the authors discuss how there are conditions under which leveraging additional test-time computational resources can be more effective than simply scaling the model's parameters. They conclude that while challenging questions generally require additional pretraining compute, LLMs can significantly benefit from optimized test-time strategies on easier and intermediate problems, promoting a shift in focus from purely scaling pretraining to improving inference capabilities [1].

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Summarise https://arxiv.org/pdf/2408.03314

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