{"entry_id": "4a8d8d75-75a8-4e61-9413-03053b721ba2", "infos": [{"txt": "R2来袭？", "language": "chinese", "position": {"atoms": [{"position_id": 0, "txt": "R2来袭？", "x": ""}]}, "tags": [], "label": "O", "web_segment_id": 0, "global_sentence_id": 0, "edu_l1_label": "EDU_O"}, {"txt": "DeepSeek与清华联手，开启大模型推理新时代", "language": "chinese", "position": {"atoms": [{"position_id": 0, "txt": "DeepSeek与清华联手，开启大模型推理新时代", "x": ""}]}, "tags": [], "label": "O", "web_segment_id": 0, "global_sentence_id": 1, "edu_l1_label": "EDU_O"}, {"txt": "            R2来袭？", "language": "chinese", "position": {"atoms": [{"position_id": 597, "txt": "\n            \nR2来袭？", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/h1"}]}, "tags": ["h1"], "label": "article_title", "web_segment_id": 37, "global_sentence_id": 2, "edu_l1_label": "BOT"}, {"txt": "DeepSeek与清华联手，开启大模型推理新时代          ", "language": "chinese", "position": {"atoms": [{"position_id": 598, "txt": "DeepSeek与清华联手，开启大模型推理新时代\n          ", "x": 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"chinese", "position": {"atoms": [{"position_id": 618, "txt": "（一）自我原则点评调优", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[7]/span/span"}]}, "tags": [], "label": "title2", "web_segment_id": 46, "global_sentence_id": 11, "edu_l1_label": "IOS"}, {"txt": "传统强化学习（RL）依赖固定规则或人工标注的奖励信号，但面对复杂场景时，模型往往难以灵活适应。", "language": "chinese", "position": {"atoms": [{"position_id": 620, "txt": "传统强化学习（RL）依赖固定规则或人工标注的奖励信号，但面对复杂场景时，模型往往难以灵活适应。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[8]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 47, "global_sentence_id": 12, "edu_l1_label": "IOS"}, {"txt": "SPCT的核心思想是让模型在推理过程中动态生成评判原则，并基于这些原则优化输出，从而实现“边推理边学习”的闭环。", "language": "chinese", "position": {"atoms": [{"position_id": 621, "txt": "SPCT的核心思想是让模型在推理过程中动态生成评判原则，并基于这些原则优化输出，从而实现“边推理边学习”的闭环。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[8]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 47, "global_sentence_id": 13, "edu_l1_label": "IOS"}, {"txt": "技术实现分为两阶段：", "language": "chinese", "position": {"atoms": [{"position_id": 623, "txt": "技术实现分为两阶段：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[9]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 48, "global_sentence_id": 14, "edu_l1_label": "IOS"}, {"txt": "1、拒绝式微调（Rejective Fine-Tuning）：", "language": "chinese", "position": {"atoms": [{"position_id": 625, "txt": "1、拒绝式微调（Rejective Fine-Tuning）：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[10]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 49, "global_sentence_id": 15, "edu_l1_label": "IOS"}, {"txt": "通过筛选高质量数据冷启动，确保模型初步生成的原则与点评符合基本逻辑。", "language": "chinese", "position": {"atoms": [{"position_id": 626, "txt": "通过筛选高质量数据冷启动，确保模型初步生成的原则与点评符合基本逻辑。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[10]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 49, "global_sentence_id": 16, "edu_l1_label": "IOS"}, {"txt": "例如，在数学推理任务中，模型会剔除错误答案或过于简单的样本，仅保留有效轨迹进行训练。", "language": "chinese", "position": {"atoms": [{"position_id": 628, "txt": "例如，在数学推理任务中，模型会剔除错误答案或过于简单的样本，仅保留有效轨迹进行训练。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[10]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 49, "global_sentence_id": 17, "edu_l1_label": "IOS"}, {"txt": "2、基于规则的强化学习（Rule-based RL）：", "language": "chinese", "position": {"atoms": [{"position_id": 630, "txt": "2、基于规则的强化学习（Rule-based RL）：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[11]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 50, "global_sentence_id": 18, "edu_l1_label": "IOS"}, {"txt": "引入动态奖励机制，模型根据生成的原则和点评内容实时调整策略。", "language": "chinese", "position": {"atoms": [{"position_id": 631, "txt": "引入动态奖励机制，模型根据生成的原则和点评内容实时调整策略。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[11]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 50, "global_sentence_id": 19, "edu_l1_label": "IOS"}, {"txt": "例如，在代码生成任务中，若模型输出的原则未能正确区分最优解，系统会通过对比真实奖励信号进行参数优化，逐步提升推理精度。", "language": "chinese", "position": {"atoms": [{"position_id": 633, "txt": "例如，在代码生成任务中，若模型输出的原则未能正确区分最优解，系统会通过对比真实奖励信号进行参数优化，逐步提升推理精度。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[11]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 50, "global_sentence_id": 20, "edu_l1_label": "IOS"}, {"txt": "优势对比传统方法：", "language": "chinese", "position": {"atoms": [{"position_id": 635, "txt": "优势对比传统方法：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[12]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 51, "global_sentence_id": 21, "edu_l1_label": "IOS"}, {"txt": "1、灵活性：", "language": "chinese", "position": {"atoms": [{"position_id": 637, "txt": "1、灵活性：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[13]/span/span[1]"}]}, "tags": [], "label": "content", 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"/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[14]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 53, "global_sentence_id": 25, "edu_l1_label": "IOS"}, {"txt": "（二）元奖励模型", "language": "chinese", "position": {"atoms": [{"position_id": 645, "txt": "（二）元奖励模型", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[15]/span/span"}]}, "tags": [], "label": "title2", "web_segment_id": 54, "global_sentence_id": 26, "edu_l1_label": "IOS"}, {"txt": "在复杂任务中，单次奖励信号可能因噪声或偏见导致评估偏差。", "language": "chinese", "position": {"atoms": [{"position_id": 647, "txt": "在复杂任务中，单次奖励信号可能因噪声或偏见导致评估偏差。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[16]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 55, "global_sentence_id": 27, "edu_l1_label": "IOS"}, {"txt": "元奖励模型通过多层级投票机制，整合多个奖励信号，筛选出最优解。", "language": "chinese", "position": {"atoms": [{"position_id": 648, "txt": "元奖励模型通过多层级投票机制，整合多个奖励信号，筛选出最优解。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[16]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 55, "global_sentence_id": 28, "edu_l1_label": "IOS"}, {"txt": "其核心流程包括：", "language": "chinese", "position": {"atoms": [{"position_id": 649, "txt": "其核心流程包括：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[16]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 55, "global_sentence_id": 29, "edu_l1_label": "IOS"}, {"txt": "1、多原则生成与投票：", "language": "chinese", "position": {"atoms": [{"position_id": 651, "txt": "1、多原则生成与投票：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[17]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 56, "global_sentence_id": 30, "edu_l1_label": "IOS"}, {"txt": "模型针对同一问题生成多个原则和点评，通过求和或加权计算综合得分。", "language": "chinese", "position": {"atoms": [{"position_id": 653, "txt": "模型针对同一问题生成多个原则和点评，通过求和或加权计算综合得分。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[17]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 56, "global_sentence_id": 31, "edu_l1_label": "IOS"}, {"txt": "2、低质量样本过滤：", "language": "chinese", "position": {"atoms": [{"position_id": 655, "txt": "2、低质量样本过滤：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[18]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 57, "global_sentence_id": 32, "edu_l1_label": "IOS"}, {"txt": "元奖励模型对生成的奖励进行二次评估，剔除低质量或存在偏见的样本，确保最终输出的可靠性。", "language": "chinese", "position": {"atoms": [{"position_id": 657, "txt": "元奖励模型对生成的奖励进行二次评估，剔除低质量或存在偏见的样本，确保最终输出的可靠性。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[18]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 57, "global_sentence_id": 33, "edu_l1_label": "IOS"}, {"txt": "实验效果：", "language": "chinese", "position": {"atoms": [{"position_id": 659, "txt": "实验效果：", "x": 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"web_segment_id": 63, "global_sentence_id": 39, "edu_l1_label": "BOS"}, {"txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/1tCrCC4sUMJ924s9bA3gT7JP.png", "language": "chinese", "position": {"atoms": [{"position_id": 141, "txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/1tCrCC4sUMJ924s9bA3gT7JP.png", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[25]/span/img"}]}, "tags": ["img"], "label": "figure", "web_segment_id": 64, "global_sentence_id": 40, "edu_l1_label": "EDU_O"}, {"txt": "（一）突破传统训练范式", "language": "chinese", "position": {"atoms": [{"position_id": 669, "txt": "（一）突破传统训练范式", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[26]/span/span"}]}, "tags": [], "label": "title2", "web_segment_id": 65, "global_sentence_id": 41, "edu_l1_label": "IOS"}, {"txt": "传统大模型依赖训练阶段的参数调整，而SPCT实现了推理阶段的实时优化。", "language": "chinese", "position": {"atoms": [{"position_id": 671, "txt": "传统大模型依赖训练阶段的参数调整，而SPCT实现了推理阶段的实时优化。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[27]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 66, "global_sentence_id": 42, "edu_l1_label": "IOS"}, {"txt": "例如，在处理长文本时，模型可通过动态生成原则，自适应调整生成策略，无需重新训练即提升任务表现。", "language": "chinese", "position": {"atoms": [{"position_id": 672, "txt": "例如，在处理长文本时，模型可通过动态生成原则，自适应调整生成策略，无需重新训练即提升任务表现。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[27]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 66, "global_sentence_id": 43, "edu_l1_label": "IOS"}, {"txt": "（二）成本与性能的“双重革命”", "language": "chinese", "position": {"atoms": [{"position_id": 674, "txt": "（二）成本与性能的“双重革命”", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[28]/span/span"}]}, "tags": [], "label": "title2", "web_segment_id": 67, "global_sentence_id": 44, "edu_l1_label": "IOS"}, {"txt": "1、低成本训练：", "language": "chinese", "position": {"atoms": [{"position_id": 676, "txt": "1、低成本训练：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[29]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 68, "global_sentence_id": 45, "edu_l1_label": "IOS"}, {"txt": "采用FP8混合精度与多令牌预测技术，DeepSeek-GRM的训练成本仅为同类模型的1/5。", "language": "chinese", "position": {"atoms": [{"position_id": 678, "txt": "采用FP8混合精度与多令牌预测技术，DeepSeek-GRM的训练成本仅为同类模型的1/5。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[29]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 68, "global_sentence_id": 46, "edu_l1_label": "IOS"}, {"txt": "2、高效推理：", "language": "chinese", "position": {"atoms": [{"position_id": 680, "txt": "2、高效推理：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[30]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 69, "global_sentence_id": 47, "edu_l1_label": "IOS"}, {"txt": "通过元奖励模型的投票机制，仅需8次采样即可达到最优性能，大幅降低计算资源消耗。", "language": "chinese", "position": {"atoms": [{"position_id": 682, "txt": "通过元奖励模型的投票机制，仅需8次采样即可达到最优性能，大幅降低计算资源消耗。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[30]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 69, "global_sentence_id": 48, "edu_l1_label": "IOS"}, {"txt": "（三）开源生态的“催化剂”", "language": "chinese", "position": {"atoms": [{"position_id": 684, "txt": "（三）开源生态的“催化剂”", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[31]/span/span"}]}, "tags": [], "label": "title2", "web_segment_id": 70, "global_sentence_id": 49, "edu_l1_label": "IOS"}, {"txt": "论文中公布的框架可直接应用于现有模型升级。", "language": "chinese", "position": {"atoms": [{"position_id": 686, "txt": "论文中公布的框架可直接应用于现有模型升级。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[32]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 71, "global_sentence_id": 50, "edu_l1_label": "IOS"}, {"txt": "例如，基于Gemma-2-27B微调的DeepSeek-GRM-27B已开源，为开发者提供了低门槛优化工具。", "language": "chinese", "position": {"atoms": [{"position_id": 687, "txt": "例如，基于Gemma-2-27B微调的DeepSeek-GRM-27B已开源，为开发者提供了低门槛优化工具。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[32]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 71, "global_sentence_id": 51, "edu_l1_label": "IOS"}, {"txt": "清华大学教授沈阳评价称，该技术“加速了AI技术民主化进程”。", "language": "chinese", "position": {"atoms": [{"position_id": 688, "txt": "清华大学教授沈阳评价称，该技术“加速了AI技术民主化进程”。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[32]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 71, "global_sentence_id": 52, "edu_l1_label": "IOS"}, {"txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/4os0dD5pPbhNRCzf13wmVr.png", "language": "chinese", "position": {"atoms": [{"position_id": 166, "txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/4os0dD5pPbhNRCzf13wmVr.png", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[33]/img"}]}, "tags": ["img"], "label": "figure", "web_segment_id": 72, 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{"txt": "虽然此次发布的论文未明确提及R2，但多方线索表明其即将面世：", "language": "chinese", "position": {"atoms": [{"position_id": 697, "txt": "虽然此次发布的论文未明确提及R2，但多方线索表明其即将面世：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[38]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 77, "global_sentence_id": 59, "edu_l1_label": "IOS"}, {"txt": "一是SPCT与元奖励模型的负载均衡策略，与DeepSeek前代模型R1的知识蒸馏技术一脉相承，为迭代奠定基础。", "language": "chinese", "position": {"atoms": [{"position_id": 698, "txt": "一是SPCT与元奖励模型的负载均衡策略，与DeepSeek前代模型R1的知识蒸馏技术一脉相承，为迭代奠定基础。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[38]/span/span"}]}, "tags": [], "label": "content", "web_segment_id": 77, "global_sentence_id": 60, "edu_l1_label": "IOS"}, {"txt": "二是OpenAI近期宣布将开源推理模型，被解读为应对R2的竞争压力。", "language": "chinese", "position": {"atoms": [{"position_id": 699, "txt": "二是OpenAI近期宣布将开源推理模型，被解读为应对R2的竞争压力。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[38]/span/span"}]}, "tags": [], "label": "content", 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R1依赖强化学习（RL）和人类反馈（RLHF），需大量人工标注，且在复杂推理任务中易陷入局部最优。", "language": "chinese", "position": {"atoms": [{"position_id": 707, "txt": "DeepSeek R1依赖强化学习（RL）和人类反馈（RLHF），需大量人工标注，且在复杂推理任务中易陷入局部最优。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[41]/span/span[2]"}]}, "tags": [], "label": "content", "web_segment_id": 80, "global_sentence_id": 65, "edu_l1_label": "IOS"}, {"txt": "R2突破：", "language": "chinese", "position": {"atoms": [{"position_id": 709, "txt": "R2突破：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[42]/span/span[1]"}]}, "tags": [], "label": "content", "web_segment_id": 81, "global_sentence_id": 66, "edu_l1_label": "IOS"}, {"txt": "SPCT让模型自主生成评价原则，实现“理解问题→动态分析→优化输出”的闭环，在数学竞赛类任务中超越GPT-4o。", "language": "chinese", "position": {"atoms": [{"position_id": 711, "txt": "SPCT让模型自主生成评价原则，实现“理解问题→动态分析→优化输出”的闭环，在数学竞赛类任务中超越GPT-4o。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[42]/span/span[2]"}]}, "tags": [], "label": "content", 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# R2来袭？DeepSeek与清华联手，开启大模型推理新时代
## 两项核心技术的原理
### （一）自我原则点评调优
#### 技术实现分为两阶段：
#### 优势对比传统方法：
### （二）元奖励模型
#### 实验效果：
## 两项核心技术的优势
### （一）突破传统训练范式
### （二）成本与性能的“双重革命”
### （三）开源生态的“催化剂”
## R2模型的猜想
### （一）R2可能具备的优势预测
#### 1、推理能力跃升
#### 2、成本效率革命
#### 3、泛化能力增强
### （二）R2可能带来的对AI行业的冲击
## 畅想R2之后的AI新图景
### （一）技术融合趋势
### （二）伦理与治理挑战