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相关论文: On The Planning Abilities of OpenAI's o1 Models: F…

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The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI research since its inception. With the advent of large…

人工智能 · 计算机科学 2024-10-04 Karthik Valmeekam , Kaya Stechly , Atharva Gundawar , Subbarao Kambhampati

The ability to plan a course of action that achieves a desired state of affairs has long been considered a core competence of intelligent agents and has been an integral part of AI research since its inception. With the advent of large…

人工智能 · 计算机科学 2024-09-23 Karthik Valmeekam , Kaya Stechly , Subbarao Kambhampati

Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition. The latest model, OpenAI's o1, stands out as the first LLM with an…

计算与语言 · 计算机科学 2024-09-24 Yunfei Xie , Juncheng Wu , Haoqin Tu , Siwei Yang , Bingchen Zhao , Yongshuo Zong , Qiao Jin , Cihang Xie , Yuyin Zhou

Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, merely increasing the number of model parameters yields…

The performance of large language models (LLMs) has recently improved to the point where models can perform well on many language tasks. We show here that--for the first time--the models can also generate valid metalinguistic analyses of…

计算与语言 · 计算机科学 2025-07-14 Gašper Beguš , Maksymilian Dąbkowski , Ryan Rhodes

In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues…

计算与语言 · 计算机科学 2024-10-07 R. Thomas McCoy , Shunyu Yao , Dan Friedman , Mathew D. Hardy , Thomas L. Griffiths

Run-time steering strategies like Medprompt are valuable for guiding large language models (LLMs) to top performance on challenging tasks. Medprompt demonstrates that a general LLM can be focused to deliver state-of-the-art performance on…

计算与语言 · 计算机科学 2024-11-07 Harsha Nori , Naoto Usuyama , Nicholas King , Scott Mayer McKinney , Xavier Fernandes , Sheng Zhang , Eric Horvitz

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some…

人工智能 · 计算机科学 2025-02-19 Mohamed Aghzal , Erion Plaku , Gregory J. Stein , Ziyu Yao

Recent developments, particularly OpenAI's O1 model, have demonstrated the remarkable potential of Large Language Models (LLMs) for complex reasoning tasks. Through analysis of O1's outputs and provided sample Chain-of-Thought (CoT)…

人工智能 · 计算机科学 2024-12-09 Toby Simonds , Jey Han Lau , Chaithanya Bandi

This paper investigates the mathematical reasoning capabilities of large language models (LLMs) using 50 newly constructed high-school-level word problems. Unlike prior studies that focus solely on answer correctness, we rigorously analyze…

人工智能 · 计算机科学 2025-02-24 Johan Boye , Birger Moell

Large Reasoning Models (LRMs) such as DeepSeek-R1 and OpenAI o1 have demonstrated remarkable capabilities in various reasoning tasks. Their strong capability to generate and reason over intermediate thoughts has also led to arguments that…

计算与语言 · 计算机科学 2025-10-17 Saurabh Srivastava , Ziyu Yao

Planning for an upcoming project iteration (sprint) is one of the key activities in Scrum planning. In this paper, we present our work in progress on exploring the applicability of Large Language Models (LLMs) for solving this problem. We…

软件工程 · 计算机科学 2025-12-23 Yuwon Yoon , Kevin Iwan , Madeleine Zwart , Xiaohan Qin , Hina Lee , Maria Spichkova

Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging these models to tackle complex reasoning tasks. Researchers…

We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early…

We examine the reasoning and planning capabilities of large language models (LLMs) in solving complex tasks. Recent advances in inference-time techniques demonstrate the potential to enhance LLM reasoning without additional training by…

人工智能 · 计算机科学 2025-02-19 Shubham Parashar , Blake Olson , Sambhav Khurana , Eric Li , Hongyi Ling , James Caverlee , Shuiwang Ji

Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-R1, and Alibaba's QwQ, have redefined AI's problem-solving capabilities by extending LLMs with advanced reasoning…

Large Language Models (LLMs), typified by OpenAI's GPT, have marked a significant advancement in artificial intelligence. Trained on vast amounts of text data, LLMs are capable of understanding and generating human-like text across a…

人工智能 · 计算机科学 2024-10-29 Haochen Zhang , Yuyang Dong , Chuan Xiao , Masafumi Oyamada

Recently, long-thought reasoning LLMs, such as OpenAI's O1, adopt extended reasoning processes similar to how humans ponder over complex problems. This reasoning paradigm significantly enhances the model's problem-solving abilities and has…

计算与语言 · 计算机科学 2025-01-30 Haotian Luo , Li Shen , Haiying He , Yibo Wang , Shiwei Liu , Wei Li , Naiqiang Tan , Xiaochun Cao , Dacheng Tao

Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference…

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