리미

https://arxiv.org/abs/2408.16314 ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual GroundingVisual grounding aims to localize the object referred to in an image based on a natural language query. Although progress has been made recently, accurately localizing target objects within multiple-instance distractions (multiple objects of the same categarxiv.org (방법론..

https://arxiv.org/abs/2505.22334 Advancing Multimodal Reasoning via Reinforcement Learning with Cold StartRecent advancements in large language models (LLMs) have demonstrated impressive chain-of-thought reasoning capabilities, with reinforcement learning (RL) playing a crucial role in this progress. While "aha moment" patterns--where models exhibit self-correarxiv.org IntroductionLLM은 뛰어난 CoT 추..

https://www.youtube.com/watch?v=127lV0wcDmc&list=PLQIgLu3Wf-q_Ne8vv-ZXuJ4mztHJaQb_v&index=6 Text Splitter : 토큰 제한이 있는 LLM이 여러 문장을 참고해 답변할 수 있도록 문서를 분할하는 역할Chunk 하나당 하나의 Vector 생성대부분의 경우에 RecursiveCharacterTextSplitter를 통해 분할CharacterTextSplitter가장 간단한 텍스트 분할기로, 특정 구분자를 기준으로 텍스트를 여러 개로 분할from langchain.text_splitter import CharacterTextSplittertext_splitter = CharacterTextSplitter( separator =..

https://www.youtube.com/watch?v=tIU2tw3PMUE&list=PLQIgLu3Wf-q_Ne8vv-ZXuJ4mztHJaQb_v&index=5 RAG : 외부 데이터를 참조하여 LLM이 답변할 수 있도록 해주는 프레임워크 Document Loaders : 다양한 형태의 문서를 RAG 전용 객체로 불러들이는 모듈Page_content : 문서의 내용Metadata : 문서의 위치, 제목, 페이지 넘버 등 URL Document Loaderfrom langchain.document_loaders import WebBaseLoaderloader = WebBaseLoader("경로")data = loader.load()from langchain.document_loaders import..

https://arxiv.org/abs/2005.11401 Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksLarge pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limarxiv.orgIntroduction사전 학습된 신경 언어 모델들은 데이터로부터..

https://arxiv.org/abs/1811.12560 An Introduction to Deep Reinforcement LearningDeep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL openarxiv.org 일반화는 다음 두 가지로 정의된다. 같은 환경에서의 일반화훈련 데이터는 일부만 있고, 전체 상태..

https://arxiv.org/abs/1811.12560 An Introduction to Deep Reinforcement LearningDeep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL openarxiv.orgPure model-based methods환경 모델이 명시적으로 주어지는 경우는 따로 학습할 필..

https://arxiv.org/abs/1811.12560 An Introduction to Deep Reinforcement LearningDeep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL openarxiv.org1. Stochastic Policy Gradient강화학습의 목표는 누적 보상을 최대화하는 최적..

https://arxiv.org/abs/1811.12560 An Introduction to Deep Reinforcement LearningDeep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL openarxiv.org 1. Q-learningQ-learning은 강화학습에서 최적 정책을 찾기 위해 행동의 가치를 ..