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從 CoT 到 RAP：用世界模型增強大模型推理論文標題：Reasoning with Language Model is Planning with World Model論文鏈接：http...
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Memory-R1：用強化學(xué)習(xí)讓大模型智能體學(xué)會管理長期記憶論文標題：Memory-R1: Enhancing Large Language Model Agents to Manage and Util...
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Reflexion：讓語言智能體通過語言反饋自我強化論文標題：Reflexion: Language Agents with Verbal Reinforcement Learning論文鏈接：h...
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ToolRL：工具調(diào)用不是格式模仿，而是獎勵學(xué)習(xí)論文標題：ToolRL: Reward is All Tool Learning Needs論文鏈接：https://arxiv.org/abs...
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ACL 2025 - 基于片段監(jiān)督偏好優(yōu)化的字幕翻譯時延對齊論文標題：Fine-grained Video Dubbing Duration Alignment with Segment Supervis...
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直接偏好優(yōu)化技術(shù)DPO基礎(chǔ)理論及推導(dǎo)
論文標題：Direct Preference Optimization: Your Language Model is Secretly a R...
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自適應(yīng)視圖增強的謠言檢測圖對比學(xué)習(xí)方法論文標題：Propagation Tree Is Not Deep: Adaptive Graph Contrastive Learning A...
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生成式大模型的RLHF技術(shù)（一）：基礎(chǔ)一、概述 大語言模型（LLMs）在預(yù)訓(xùn)練的過程中通常會捕捉數(shù)據(jù)的特征，而這些訓(xùn)練數(shù)據(jù)通常既包含高質(zhì)量的也包含低質(zhì)量的，因此模型有時會產(chǎn)生不被期望...
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LoRA：大模型下游任務(wù)的低秩適應(yīng)論文標題：LoRA: Low-Rank Adaptation of Large Language Models論文鏈接：https://arxi...