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【第141期】O1 Replication Journey：Part 3
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：O1 Replication Journey -- Part 3: Inference-time Scaling for Medical ReasoningSummary
This research paper investigates the effectiveness of inference-time scaling in large language models (LLMs) for medical reasoning tasks. The authors explore how increasing the processing time during inference improves the accuracy of LLMs on complex medical benchmarks like MedQA and JAMA Clinical Challenges. They introduce a novel journey learning approach, using knowledge distillation to generate high-quality training data for improved reasoning chains. Their experiments show that longer inference times correlate with better performance, especially for more challenging tasks, though sufficient LLM capacity is crucial. The study also examines the utility of majority voting as a means to scale inference-time computations.
这篇研究论文探讨了推理时扩展在大型语言模型（LLMs）在医学推理任务中的有效性。作者研究了在推理过程中增加处理时间如何提高LLMs在复杂医学基准任务（如MedQA和JAMA临床挑战）上的准确性。他们提出了一种新颖的“旅程学习”方法，利用知识蒸馏生成高质量的训练数据，以改善推理链条。实验结果表明，较长的推理时间与更好的性能相关，尤其是在面对更具挑战性的任务时，尽管足够的LLM容量至关重要。研究还探讨了多数投票作为扩展推理时计算的一种手段的有效性。
原文链接：https://arxiv.org/abs/2501.06458
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：O1 Replication Journey -- Part 3: Inference-time Scaling for Medical ReasoningSummary
This research paper investigates the effectiveness of inference-time scaling in large language models (LLMs) for medical reasoning tasks. The authors explore how increasing the processing time during inference improves the accuracy of LLMs on complex medical benchmarks like MedQA and JAMA Clinical Challenges. They introduce a novel journey learning approach, using knowledge distillation to generate high-quality training data for improved reasoning chains. Their experiments show that longer inference times correlate with better performance, especially for more challenging tasks, though sufficient LLM capacity is crucial. The study also examines the utility of majority voting as a means to scale inference-time computations.
这篇研究论文探讨了推理时扩展在大型语言模型（LLMs）在医学推理任务中的有效性。作者研究了在推理过程中增加处理时间如何提高LLMs在复杂医学基准任务（如MedQA和JAMA临床挑战）上的准确性。他们提出了一种新颖的“旅程学习”方法，利用知识蒸馏生成高质量的训练数据，以改善推理链条。实验结果表明，较长的推理时间与更好的性能相关，尤其是在面对更具挑战性的任务时，尽管足够的LLM容量至关重要。研究还探讨了多数投票作为扩展推理时计算的一种手段的有效性。
原文链接：https://arxiv.org/abs/2501.06458