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英文字典中文字典相关资料:


  • Understanding Reasoning LLMs - sebastianraschka. com
    In this section, I will outline the key techniques currently used to enhance the reasoning capabilities of LLMs and to build specialized reasoning models such as DeepSeek-R1, OpenAI’s o1 o3, and others
  • samkhur006 awesome-llm-planning-reasoning - GitHub
    Techniques: Innovative methods that enable LLMs to reason and plan effectively, such as Chain-of-Thought prompting and Tree of Thoughts Reasoning Limitations: Critical investigations that explore the limitations and challenges LLMs face in planning and reasoning tasks
  • Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research
    We introduce Agentic Reasoning, a framework that enhances large language model (LLM) reasoning by integrating external tool-using agents
  • Code Generation with LLMs - Stanford University
    HumanEval (Chen et al , 2021) is the most widely-recognized research benchmark for code generation the first major code-specific LLM HumanEval is 164 handwritten programming problems, each with several unit tests
  • How Reasoning Models are transforming Logical AI thinking
    Reasoning models are a new category of specialized language models They are designed to break down complex problems into smaller, manageable steps and solve them through explicit logical reasoning (This step is also called “thinking”)
  • DeepSeek-R1 Paper Explained – A New RL LLMs Era in AI?
    The paper, titled “DeepSeek-R1: Incentivizing Reasoning Capability in Large Language Models via Reinforcement Learning”, presents a state-of-the-art, open-source reasoning model and a detailed recipe for training such models using large-scale reinforcement learning techniques
  • Learning to reason with LLMs - OpenAI
    We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning o1 thinks before it answers—it can produce a long internal chain of thought before responding to the user
  • RUCAIBox Slow_Thinking_with_LLMs - GitHub
    Slow-thinking reasoning systems, such as o1, have demonstrated remarkable capabilities in solving complex reasoning tasks, and are primarily developed and maintained by industry, with their core techniques not publicly disclosed This paper presents a reproduction report on implementing o1-like reasoning systems
  • Mathematical Reasoning Through LLM Finetuning - Stanford University
    Using LLMs to generate step-by-step solutions to math problems can be extremely dificult due to the logical reasoning required In this paper, we explore several different methods to finetune LLMs for this task
  • What We Learned from a Year of Building with LLMs (Part I)
    It dives into the tactical nuts and bolts of working with LLMs We share best practices and common pitfalls around prompting, setting up retrieval-augmented generation, applying flow engineering, and evaluation and monitoring





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