Scaling LLM Test-Time Compute Optimally

investigates the scaling behaviors of inference-time computation in LLMs; in particular, it analyses how much an LLM can be improved provided a fixed amount of inference-time compute; finds that the effectiveness of different scaling approaches varies by difficulty of prompt; it then proposes an adaptive compute-optimal strategy that can improve efficiency by more than 4x compared to a best-of-N baseline; reports that in a FLOPs-matched evaluation, optimally scaling test-time compute can outperform a 14x larger model.