Berkeley
This AI Paper from UC Berkeley Introduces a Data-Efficient Approach to Long Chain-of-Thought Reasoning for Large Language Models
Large language models (LLMs) process extensive datasets to generate coherent outputs, focusing on refining chain-of-thought (CoT) reasoning. This methodology enables models to break down intricate problems into sequential steps, closely emulating human-like logical reasoning. Generating structured reasoning responses has been a major challenge, often requiring extensive computational resources and large-scale datasets to achieve optimal performance….