Smaller LLMs Outperform Large Models In Reasoning Tasks
Smaller LLMs outperform larger models in reasoning tasks with "compute-optimal sampling" training approach, reducing model size & compute requirements while maintaining performance.
This is a Plain English Papers summary of a research paper called Compute-Optimal Sampling: Smaller LLMs Outperform Large Models in Reasoning Tasks. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview Presents a novel training approach, "compute-optimal sampling," to improve the reasoning abilities of large language models (LLMs) while reducing their model size and compute requirements. Demonstrates that this approach can produce smaller, weaker LLMs that outperform larger, more powerful models on a range of reasoning tasks. Suggests th...