Paper: https://arxiv.org/abs/2411.00412 This research introduces a novel two-stage training method to improve Large Language Models' (LLMs) ability to solve complex scientific problems. The method, called Adapting While Learning (AWL), first distills world knowledge into the LLM via supervised fine-tuning. Then, it adapts tool usage by classifying problems as easy or hard, using direct reasoning for easy problems and tools for hard ones. Experiments across various scientific datasets show significant improvements in both answer accuracy and tool usage precision, surpassing several state-of-the-art LLMs. The study also explores extensions to open-ended questions and robustness to noisy data. ai , artificial intelligence , arxiv , research , paper , publication , llm, genai, generative ai , large visual models, large language models, large multi modal models, nlp, text, machine learning, ml, nividia, openai, anthropic, microsoft, google, technology, cutting-edge, meta, llama, chatgpt, gpt, elon musk, sam altman, deployment, engineering, scholar, science