
Sign up to save your podcasts
Or
Seventy3:借助NotebookLM的能力进行论文解读,专注人工智能、大模型、机器人算法方向,让大家跟着AI一起进步。
进群添加小助手微信:seventy3_podcast
备注:小宇宙
今天的主题是:Competitive Programming with Large Reasoning ModelsSummary
This document from OpenAI explores the advancements of large reasoning models in competitive programming and software engineering. It details the development and evaluation of models like o1, o1-ioi (specialized for the International Olympiad in Informatics), and the more advanced o3. The findings indicate that scaling general-purpose reinforcement learning in these models leads to significant performance gains, even surpassing results achieved through hand-engineered, domain-specific strategies. The report highlights o3's ability to achieve top-tier results in competitive programming and its strong performance on real-world coding benchmarks, suggesting a promising direction for AI in reasoning-intensive domains.
这份来自OpenAI的文档探讨了大型推理模型在竞赛编程和软件工程领域的进展。文中详细介绍了像o1、o1-ioi(专为国际信息学奥林匹克设计)以及更先进的o3模型的开发与评估。
研究结果表明,在这些模型中,通过扩展通用强化学习,能够显著提升性能,甚至超过了通过手工设计的领域特定策略所取得的成绩。报告还重点强调了o3在竞赛编程中的卓越表现,尤其是在现实世界编码基准测试中的强大表现,表明这一方向为AI在推理密集型领域的发展提供了有前景的道路。
原文链接:https://arxiv.org/abs/2502.06807
Seventy3:借助NotebookLM的能力进行论文解读,专注人工智能、大模型、机器人算法方向,让大家跟着AI一起进步。
进群添加小助手微信:seventy3_podcast
备注:小宇宙
今天的主题是:Competitive Programming with Large Reasoning ModelsSummary
This document from OpenAI explores the advancements of large reasoning models in competitive programming and software engineering. It details the development and evaluation of models like o1, o1-ioi (specialized for the International Olympiad in Informatics), and the more advanced o3. The findings indicate that scaling general-purpose reinforcement learning in these models leads to significant performance gains, even surpassing results achieved through hand-engineered, domain-specific strategies. The report highlights o3's ability to achieve top-tier results in competitive programming and its strong performance on real-world coding benchmarks, suggesting a promising direction for AI in reasoning-intensive domains.
这份来自OpenAI的文档探讨了大型推理模型在竞赛编程和软件工程领域的进展。文中详细介绍了像o1、o1-ioi(专为国际信息学奥林匹克设计)以及更先进的o3模型的开发与评估。
研究结果表明,在这些模型中,通过扩展通用强化学习,能够显著提升性能,甚至超过了通过手工设计的领域特定策略所取得的成绩。报告还重点强调了o3在竞赛编程中的卓越表现,尤其是在现实世界编码基准测试中的强大表现,表明这一方向为AI在推理密集型领域的发展提供了有前景的道路。
原文链接:https://arxiv.org/abs/2502.06807