reasonScaling
reasonScaling is a concept and technique used in machine learning, particularly in the context of large language models (LLMs) and other complex AI systems. It addresses the challenge of efficiently scaling the reasoning capabilities of these models as their size or the complexity of the tasks they are asked to perform increases. Essentially, reasonScaling explores how to make AI systems better at thinking through problems, drawing conclusions, and making logical deductions in a way that remains computationally feasible and effective.
The core idea behind reasonScaling is not just about making models larger, but about making them smarter
Research in reasonScaling often focuses on improving performance on tasks that require multi-step reasoning, such as