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DeepSeek 有好口碑的原因在于是第一个把复现 MoE、o1 等发出来，胜在做的早，但能不能做到最好，空间还很大。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 611, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "2. DeepSeek 有好口碑的原因在于是第一个把复现 MoE、o1 等发出来，胜在做的早，但能不能做到最好，空间还很大。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[15]/span"}]}, "tags": [], "label": "content", "web_segment_id": 26, "global_sentence_id": 24, "edu_l1_label": "IOS"}, {"txt": "后面新的挑战在于资源有限，只能把有限的资源放在最亮眼的地方。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 613, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "后面新的挑战在于资源有限，只能把有限的资源放在最亮眼的地方。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[15]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 26, "global_sentence_id": 25, "edu_l1_label": "IOS"}, {"txt": "这个团队的 research 能力、团队文化还是很好的，如果再给 10万、20 万张卡，可能能做出更好的事情。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 615, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "这个团队的 research 能力、团队文化还是很好的，如果再给 10万、20 万张卡，可能能做出更好的事情。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[15]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 26, "global_sentence_id": 26, "edu_l1_label": "IOS"}, {"txt": "3. DeekSeek 从 preview 到正式发布这段时间，长上下文能力提升很快。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 617, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "3. DeekSeek 从 preview 到正式发布这段时间，长上下文能力提升很快。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[16]/span"}]}, "tags": [], "label": "content", "web_segment_id": 27, "global_sentence_id": 27, "edu_l1_label": "IOS"}, {"txt": "DeepSeek 的 Long context 10K 用非常常规的方法就能够做到。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 618, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "DeepSeek 的 Long context 10K 用非常常规的方法就能够做到。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[16]/span"}]}, "tags": [], "label": "content", "web_segment_id": 27, "global_sentence_id": 28, "edu_l1_label": "IOS"}, {"txt": "4. 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智能。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 637, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "DeepSeek 最重要的事就是 push 智能。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[19]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 30, "global_sentence_id": 36, "edu_l1_label": "IOS"}, {"txt": "钱和商业化的优先级都不高。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 638, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "钱和商业化的优先级都不高。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[19]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 30, "global_sentence_id": 37, "edu_l1_label": "IOS"}, {"txt": "中国需要有几个领先的 AI labs 来探索能 beat OpenAI 的东西，智能要走的时间很长，今年又开始分化，肯定要有新东西出来。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 640, "size": 0, 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今天确实没有什么好的商业模式，后面可能需要跑通。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[21]/span"}]}, "tags": [], "label": "content", "web_segment_id": 32, "global_sentence_id": 40, "edu_l1_label": "IOS"}, {"txt": "梁文锋是有抱负的，DeepSeek 不在乎形态，往 AGI 走就是了。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 646, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "梁文锋是有抱负的，DeepSeek 不在乎形态，往 AGI 走就是了。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[21]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 32, "global_sentence_id": 41, "edu_l1_label": "IOS"}, {"txt": "9. 读完 DeepSeek 论文的感受是，很多都是节约硬件开销的技术，在比较大的几个 scaling 方向上，DeepSeek 的技巧可以把成本降下来。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 648, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "9. 读完 DeepSeek 论文的感受是，很多都是节约硬件开销的技术，在比较大的几个 scaling 方向上，DeepSeek 的技巧可以把成本降下来。", "x": 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DeepSeek 为什么能追这么快？", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 661, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "12. 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DeepSeek-R1 一定程度上说明用 SFT 做蒸馏有很大好处。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 710, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "19. DeepSeek-R1 一定程度上说明用 SFT 做蒸馏有很大好处。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[40]/span"}]}, "tags": [], "label": "content", "web_segment_id": 53, "global_sentence_id": 79, "edu_l1_label": "IOS"}, {"txt": "DeepSeek-R1 并不是完全不做 SFT，而是在第三步骤只做了 SFT，最后一步 alignment（对齐）再用了 RLHF（基于人类反馈的强化学习）。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 711, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "DeepSeek-R1 并不是完全不做 SFT，而是在第三步骤只做了 SFT，最后一步 alignment（对齐）再用了 RLHF（基于人类反馈的强化学习）。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[40]/span"}]}, "tags": [], "label": "content", "web_segment_id": 53, "global_sentence_id": 80, "edu_l1_label": "IOS"}, {"txt": "20. R1 本质是 SFT 训练出来的，比较特殊的是数据是用 RLHF 训练出来的模型生成的，说明不需要用特别复杂的方法，只要有足够好的方法，只需要用 SFT 蒸馏就行。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 713, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "20. R1 本质是 SFT 训练出来的，比较特殊的是数据是用 RLHF 训练出来的模型生成的，说明不需要用特别复杂的方法，只要有足够好的方法，只需要用 SFT 蒸馏就行。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[41]/span"}]}, "tags": [], "label": "content", "web_segment_id": 54, "global_sentence_id": 81, "edu_l1_label": "IOS"}, {"txt": "21. GRPO 的本质在于 base model（基础模型）得足够聪明，一个 prompt 生成用了 16 个 generation，得尝试几次才能大概率有正确的答案。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 715, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "21. GRPO 的本质在于 base model（基础模型）得足够聪明，一个 prompt 生成用了 16 个 generation，得尝试几次才能大概率有正确的答案。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[42]/span"}]}, "tags": [], "label": "content", "web_segment_id": 55, "global_sentence_id": 82, "edu_l1_label": "IOS"}, {"txt": "不错的 base model 加上可以 verify，是 R1 提供的思路，math 和 coding 很合适是因为这两类任务比较容易 verify，但理论上可以在其他场景任务上做类似的过程，最终实现一个通用的 RL 模型。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 717, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "不错的 base model 加上可以 verify，是 R1 提供的思路，math 和 coding 很合适是因为这两类任务比较容易 verify，但理论上可以在其他场景任务上做类似的过程，最终实现一个通用的 RL 模型。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[42]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 55, "global_sentence_id": 83, "edu_l1_label": "IOS"}, {"txt": "22. R1 - Zero 没有用 SFT 就出现了 CoT 的过程，CoT 会越来越长，这个涌现过程很有意义，SFT 更像是一个辅助手段，模型没有 SFT 也能产生，有了 SFT 能很快生成。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 719, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "22. R1 - Zero 没有用 SFT 就出现了 CoT 的过程，CoT 会越来越长，这个涌现过程很有意义，SFT 更像是一个辅助手段，模型没有 SFT 也能产生，有了 SFT 能很快生成。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[43]/span"}]}, "tags": [], "label": "content", "web_segment_id": 56, "global_sentence_id": 84, "edu_l1_label": "IOS"}, {"txt": "23. 这件事说明现在很多小模型厂商可以用 SFT 去蒸馏大模型，并且效果会很好，但也没有在 R1 的过程中完全被抛弃。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 721, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "23. 这件事说明现在很多小模型厂商可以用 SFT 去蒸馏大模型，并且效果会很好，但也没有在 R1 的过程中完全被抛弃。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[44]/span"}]}, "tags": [], "label": "content", "web_segment_id": 57, "global_sentence_id": 85, "edu_l1_label": "IOS"}, {"txt": "24. 一个 LLM 集合无限长的 CoT 理论上可以看成一台图灵机，理论上通过无限长的 CoT 可以解决极复杂的计算问题（computational problem），但 CoT 本质上只是中间搜索结果，用一种优化的方式去不停 sample potential output，可能会输出正确结果，然后让模型往更可信的方向去推。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 723, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "24. 一个 LLM 集合无限长的 CoT 理论上可以看成一台图灵机，理论上通过无限长的 CoT 可以解决极复杂的计算问题（computational problem），但 CoT 本质上只是中间搜索结果，用一种优化的方式去不停 sample potential output，可能会输出正确结果，然后让模型往更可信的方向去推。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[45]/strong/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 58, "global_sentence_id": 86, "edu_l1_label": "IOS"}, {"txt": "本质上是模型为了得到这样的结果，必须要做一些 computation，CoT 是 computation 中间必须经过的中间输出，最终结果可以说是涌现，也可以说是它作为计算机的本质。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 725, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "本质上是模型为了得到这样的结果，必须要做一些 computation，CoT 是 computation 中间必须经过的中间输出，最终结果可以说是涌现，也可以说是它作为计算机的本质。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[45]/span"}]}, "tags": [], "label": "content", "web_segment_id": 58, "global_sentence_id": 87, "edu_l1_label": "IOS"}, {"txt": "25. DeepSeek 的论文里面虽然没有提到长上下文，但体感上 R1-preview 和 R1 之间模型的 context window 提升了很多，猜测是做了一些 Long2Short CoT 的提升，包括在第三阶段的 SFT 用的 CoT 最终在 generation 的时候也被去掉，最后发布的版本可能是用了更加 clean 的 CoT 数据做 SFT。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 727, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "25. DeepSeek 的论文里面虽然没有提到长上下文，但体感上 R1-preview 和 R1 之间模型的 context window 提升了很多，猜测是做了一些 Long2Short CoT 的提升，包括在第三阶段的 SFT 用的 CoT 最终在 generation 的时候也被去掉，最后发布的版本可能是用了更加 clean 的 CoT 数据做 SFT。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[46]/span"}]}, "tags": [], "label": "content", "web_segment_id": 59, "global_sentence_id": 88, "edu_l1_label": "IOS"}, {"txt": "26. SFT 的数据种类有几种：", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 729, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "26. SFT 的数据种类有几种：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[47]/span"}]}, "tags": [], "label": "content", "web_segment_id": 60, "global_sentence_id": 89, "edu_l1_label": "IOS"}, {"txt": "一个是冷启动的数据，更像是给模型一个很好的策略，给一个比较好的初始化，这样能做的探索更好，RL 中有一个优化目标是和原策略更接近；", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 730, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "一个是冷启动的数据，更像是给模型一个很好的策略，给一个比较好的初始化，这样能做的探索更好，RL 中有一个优化目标是和原策略更接近；", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[47]/span"}]}, "tags": [], "label": "content", "web_segment_id": 60, "global_sentence_id": 90, "edu_l1_label": "IOS"}, {"txt": "另一种数据是做了 RL 之后，生成很多 data，再加上别的数据，再在 base model SFT，本质上每个 domain 有自己的 data processing pipeline 之类的，这个数据的能力是从 base model 来的，蒸馏是无损的，把多个 domain 放到一起可能会有泛化。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 731, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "另一种数据是做了 RL 之后，生成很多 data，再加上别的数据，再在 base model SFT，本质上每个 domain 有自己的 data processing pipeline 之类的，这个数据的能力是从 base model 来的，蒸馏是无损的，把多个 domain 放到一起可能会有泛化。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[47]/span"}]}, "tags": [], "label": "content", "web_segment_id": 60, "global_sentence_id": 91, "edu_l1_label": "IOS"}, {"txt": "27. 不确定 R1 这个过程的数据效率怎么样。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 733, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "27. 不确定 R1 这个过程的数据效率怎么样。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[48]/span"}]}, "tags": [], "label": "content", "web_segment_id": 61, "global_sentence_id": 92, "edu_l1_label": "IOS"}, {"txt": "猜测 OpenAI 针对数据效率也做了类似的事情，比如 fine tuning。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 734, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "猜测 OpenAI 针对数据效率也做了类似的事情，比如 fine tuning。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[48]/span"}]}, "tags": [], "label": "content", "web_segment_id": 61, "global_sentence_id": 93, "edu_l1_label": "IOS"}, {"txt": "R1 第三阶段没有用 RL 做出来的模型作为 base 去训练，而是去生成了数据，再去 SFT 得到 R1，数据包含 600K 的 reasoning data 和 200K non-reasoning data。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 735, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "R1 第三阶段没有用 RL 做出来的模型作为 base 去训练，而是去生成了数据，再去 SFT 得到 R1，数据包含 600K 的 reasoning data 和 200K non-reasoning data。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[48]/span"}]}, "tags": [], "label": "content", "web_segment_id": 61, "global_sentence_id": 94, "edu_l1_label": "IOS"}, {"txt": "第二阶段的模型可能在 example 的 domain 之外但仍然需要某种 reasoning 的场景下，可能也能展示解题能力，从而得到 reasoning data。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 736, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "第二阶段的模型可能在 example 的 domain 之外但仍然需要某种 reasoning 的场景下，可能也能展示解题能力，从而得到 reasoning data。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[48]/span"}]}, "tags": [], "label": "content", "web_segment_id": 61, "global_sentence_id": 95, "edu_l1_label": "IOS"}, {"txt": "而 non reasoning data 是 V3 SFT data 的一部分，是让 V3 脑补出了一个 CoT。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 737, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "而 non reasoning data 是 V3 SFT data 的一部分，是让 V3 脑补出了一个 CoT。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[48]/span"}]}, "tags": [], "label": "content", "web_segment_id": 61, "global_sentence_id": 96, "edu_l1_label": "IOS"}, {"txt": "800K 的数据还是挺小的，挺有效率的。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 738, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "800K 的数据还是挺小的，挺有效率的。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[48]/span"}]}, "tags": [], "label": "content", "web_segment_id": 61, "global_sentence_id": 97, "edu_l1_label": "IOS"}, {"txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/1r1XWm13Dr8T2N3QINW95IQ.png", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 218, "size": 0, "start": 0, "tags": ["img"], "tail": 0, "txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/1r1XWm13Dr8T2N3QINW95IQ.png", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/p[5]/span/img"}]}, "tags": ["img"], "label": "figure", "web_segment_id": 62, "global_sentence_id": 98, "edu_l1_label": "EDU_O"}, {"txt": "技术细节2：", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 740, "size": 0, "start": 0, "tags": ["h1"], "tail": 0, "txt": "技术细节2：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/h1[7]/span"}]}, "tags": ["h1"], "label": "title2", "web_segment_id": 63, "global_sentence_id": 99, "edu_l1_label": "BOS"}, {"txt": "数据", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 741, "size": 0, "start": 0, "tags": ["h1"], "tail": 0, "txt": "数据", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/h1[7]/span"}]}, "tags": ["h1"], "label": "title2", "web_segment_id": 63, "global_sentence_id": 100, "edu_l1_label": "IOS"}, {"txt": "“DeepSeek在数据标注上非常重视”", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 743, "size": 0, "start": 0, "tags": ["h1"], "tail": 0, "txt": "“DeepSeek在数据标注上非常重视”", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/h1[8]/span"}]}, "tags": ["h1"], "label": "title1", "web_segment_id": 64, "global_sentence_id": 101, "edu_l1_label": "IOS"}, {"txt": "28. Scale.AI 不一定会失败，现在需要在各种 domain 上做 RL，比较常用的是 math 和 coding，还是需要 expert 来标注，但数据标注可能会更复杂，但市场会存在。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 745, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "28. Scale.AI 不一定会失败，现在需要在各种 domain 上做 RL，比较常用的是 math 和 coding，还是需要 expert 来标注，但数据标注可能会更复杂，但市场会存在。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[49]/span"}]}, "tags": [], "label": "content", "web_segment_id": 65, "global_sentence_id": 102, "edu_l1_label": "IOS"}, {"txt": "29. 在 training 上，多模态数据几乎看不出效果，或者说成本太高了，今天还没有任何证据说有用，未来机会可能比较大。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 747, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "29. 在 training 上，多模态数据几乎看不出效果，或者说成本太高了，今天还没有任何证据说有用，未来机会可能比较大。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[50]/span"}]}, "tags": [], "label": "content", "web_segment_id": 66, "global_sentence_id": 103, "edu_l1_label": "IOS"}, {"txt": "30. DeepSeek 在数据标注上非常重视，听说梁文锋自己也会打标签，在 AI 上除了算法和技巧，数据的精确度也很关键，特斯拉的标注成本几乎是中国自动驾驶的 20 倍，中国自动驾驶的数据经历了大而全、精细化到最终发现要找开车经验和能力特别丰富的人，这个是特斯拉一开始就在做的事。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 749, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "30. DeepSeek 在数据标注上非常重视，", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[51]/strong/span"}, {"immutable": false, "orig_start": 0, "position_id": 751, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "听说梁文锋自己也会打标签，在 AI 上除了算法和技巧，数据的精确度也很关键，特斯拉的标注成本几乎是中国自动驾驶的 20 倍，中国自动驾驶的数据经历了大而全、精细化到最终发现要找开车经验和能力特别丰富的人，这个是特斯拉一开始就在做的事。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[51]/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 67, "global_sentence_id": 104, "edu_l1_label": "IOS"}, {"txt": "特斯拉的机器人的动作是找的小脑非常健康的人做的标注，丝滑程度很好，而中国找的人的丝滑程度很差。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 752, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "特斯拉的机器人的动作是找的小脑非常健康的人做的标注，丝滑程度很好，而中国找的人的丝滑程度很差。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[51]/span"}]}, "tags": [], "label": "content", "web_segment_id": 67, "global_sentence_id": 105, "edu_l1_label": "IOS"}, {"txt": "所以 DeepSeek 在数据标注上的投入是模型效率好的关键之一。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 753, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "所以 DeepSeek 在数据标注上的投入是模型效率好的关键之一。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[51]/span"}]}, "tags": [], "label": "content", "web_segment_id": 67, "global_sentence_id": 106, "edu_l1_label": "IOS"}, {"txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/3jvQwG2uJC4g18PN57CmVom.png", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 236, "size": 0, "start": 0, "tags": ["img"], "tail": 0, "txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/3jvQwG2uJC4g18PN57CmVom.png", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/p[7]/span/img"}]}, "tags": ["img"], "label": "figure", "web_segment_id": 68, "global_sentence_id": 107, "edu_l1_label": "EDU_O"}, {"txt": "技术细节3：", "language": 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["h1"], "label": "title1", "web_segment_id": 70, "global_sentence_id": 110, "edu_l1_label": "IOS"}, {"txt": "31. 如果不去了解模型训练中最大的技术痛点，而选择用蒸馏的技术去避免了解，那么在下一代技术提出的时候，就可能会掉进坑里。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 760, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "31. 如果不去了解模型训练中最大的技术痛点，而选择用蒸馏的技术去避免了解，那么在下一代技术提出的时候，就可能会掉进坑里。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[52]/strong/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 71, "global_sentence_id": 111, "edu_l1_label": "IOS"}, {"txt": "32. 大模型和小模型能力是不匹配的，从大模型往小模型进行蒸馏是真的蒸馏，teacher to student，如果从完全不会中文的模型蒸馏各种中文数据，性能可能会下跌。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 762, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "32. 大模型和小模型能力是不匹配的，从大模型往小模型进行蒸馏是真的蒸馏，teacher to student，如果从完全不会中文的模型蒸馏各种中文数据，性能可能会下跌。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[53]/span"}]}, "tags": [], "label": "content", "web_segment_id": 72, "global_sentence_id": 112, "edu_l1_label": "IOS"}, {"txt": "但实际上蒸馏小模型确实有很明显的性能提升，R1 蒸馏出来后的模型再做 RL 会增长很多，因为是用和模型不匹配的数据做出来的。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 763, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "但实际上蒸馏小模型确实有很明显的性能提升，R1 蒸馏出来后的模型再做 RL 会增长很多，因为是用和模型不匹配的数据做出来的。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[53]/span"}]}, "tags": [], "label": "content", "web_segment_id": 72, "global_sentence_id": 113, "edu_l1_label": "IOS"}, {"txt": "33. 蒸馏的坏处是模型 diversity 下降，影响模型上限，无法超越最强的模型。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 765, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "33. 蒸馏的坏处是模型 diversity 下降，影响模型上限，无法超越最强的模型。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[54]/strong/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 73, "global_sentence_id": 114, "edu_l1_label": "IOS"}, {"txt": "但短期看，蒸馏也是一条路线。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 766, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "但短期看，蒸馏也是一条路线。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[54]/strong/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 73, "global_sentence_id": 115, "edu_l1_label": "IOS"}, {"txt": "34. 用蒸馏会有一些 hack，早期一般在 instruction 调过的模型做 RL，这个阶段模型会呈现出的特征是：", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 768, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "34. 用蒸馏会有一些 hack，早期一般在 instruction 调过的模型做 RL，这个阶段模型会呈现出的特征是：", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[55]/span"}]}, "tags": [], "label": "content", "web_segment_id": 74, "global_sentence_id": 116, "edu_l1_label": "IOS"}, {"txt": "先去生成没有用的想法，然后最后突然答对，原因在于很多 RL hack 做得非常隐晦，模型可能在预训练的时候背了很多问题，所以明面上是在思考，其实只是在靠近背的题。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 769, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "先去生成没有用的想法，然后最后突然答对，原因在于很多 RL hack 做得非常隐晦，模型可能在预训练的时候背了很多问题，所以明面上是在思考，其实只是在靠近背的题。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[55]/span"}]}, "tags": [], "label": "content", "web_segment_id": 74, "global_sentence_id": 117, "edu_l1_label": "IOS"}, {"txt": "这就是蒸馏的隐患。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 770, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "这就是蒸馏的隐患。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[55]/span"}]}, "tags": [], "label": "content", "web_segment_id": 74, "global_sentence_id": 118, "edu_l1_label": "IOS"}, {"txt": "如果不做标注就蒸馏，那现在做 具有可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）的时候，就会导致模型会用更简单的方式解决，而不是去思考这个问题 OpenAI 也没有解决。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 771, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "如果不做标注就蒸馏，那现在做 具有可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）的时候，就会导致模型会用更简单的方式解决，而不是去思考这个问题 OpenAI 也没有解决。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[55]/span"}]}, "tags": [], "label": "content", "web_segment_id": 74, "global_sentence_id": 119, "edu_l1_label": "IOS"}, {"txt": "可能是这一代技术的缺陷。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 772, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "可能是这一代技术的缺陷。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[55]/span"}]}, "tags": [], "label": "content", "web_segment_id": 74, "global_sentence_id": 120, "edu_l1_label": "IOS"}, {"txt": "35. 长期来说，通过走捷径的方式，而没有自己通过愿景去想怎么做技术方案，而是直接复现，中间可能会有不知道的坑。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 774, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "35. 长期来说，通过走捷径的方式，而没有自己通过愿景去想怎么做技术方案，而是直接复现，中间可能会有不知道的坑。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[56]/span"}]}, "tags": [], "label": "content", "web_segment_id": 75, "global_sentence_id": 121, "edu_l1_label": "IOS"}, {"txt": "比如在这一代技术 long context 没有质变的前提下，解决问题的上限可能会被限制。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 775, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "比如在这一代技术 long context 没有质变的前提下，解决问题的上限可能会被限制。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[56]/span"}]}, "tags": [], "label": "content", "web_segment_id": 75, "global_sentence_id": 122, "edu_l1_label": "IOS"}, {"txt": "R1-zero 可能是一个正确的方向，从头就做 R1-zero 或不通过类 o1 的数据启动可能更好。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 776, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "R1-zero 可能是一个正确的方向，从头就做 R1-zero 或不通过类 o1 的数据启动可能更好。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[56]/span"}]}, "tags": [], "label": "content", "web_segment_id": 75, "global_sentence_id": 123, "edu_l1_label": "IOS"}, {"txt": "照着别人的技术方案可能不太好，希望更多探索。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 777, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "照着别人的技术方案可能不太好，希望更多探索。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[56]/span"}]}, "tags": [], "label": "content", "web_segment_id": 75, "global_sentence_id": 124, "edu_l1_label": "IOS"}, {"txt": "36. 其他模型用蒸馏也能得到较好的结果，未来在模型生态里面可能就会有老师、学生的角色区分，有能力当一名好学生也是一种可以的商业模式。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 779, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, 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DeepSeek 之所以受关注，更多是开源和闭源路线之争。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 918, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "75. 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DeepSeek 成本上做了很多优化，Amazon 等还没有看到因此做出的改变，还是按照既定的计划做，目前是一个共存的状态。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 923, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "77. DeepSeek 成本上做了很多优化，Amazon 等还没有看到因此做出的改变，还是按照既定的计划做，目前是一个共存的状态。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[98]/span"}]}, "tags": [], "label": "content", "web_segment_id": 138, "global_sentence_id": 213, "edu_l1_label": "IOS"}, {"txt": "开源和闭源模型并不矛盾，高校和小 lab 应该会优先选择 DeepSeek，不会对云厂商有竞争，因为云厂商对开源、闭源都是支持的，生态不会改变，目前也是共存状态。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 924, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "开源和闭源模型并不矛盾，高校和小 lab 应该会优先选择 DeepSeek，不会对云厂商有竞争，因为云厂商对开源、闭源都是支持的，生态不会改变，目前也是共存状态。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[98]/span"}]}, "tags": [], "label": "content", "web_segment_id": 138, "global_sentence_id": 214, "edu_l1_label": "IOS"}, {"txt": "DeepSeek 在 tool use 等上面还没有像 Anthropic 这么成熟，以及后者已经花了很多时间在 AI 安全上，DeepSeek 如果长期希望得到欧美市场的认可，是需要考虑的。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 925, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "DeepSeek 在 tool use 等上面还没有像 Anthropic 这么成熟，以及后者已经花了很多时间在 AI 安全上，DeepSeek 如果长期希望得到欧美市场的认可，是需要考虑的。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[98]/span"}]}, "tags": [], "label": "content", "web_segment_id": 138, "global_sentence_id": 215, "edu_l1_label": "IOS"}, {"txt": "78. 开源对整个市场的 margin 是有控制的，如果开源能做到闭源的 95%，那如果闭源太贵，那完全就可以用开源来做，如果开源和闭源能力差不多，那对闭源是一个很大的挑战。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 927, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "78. 开源对整个市场的 margin 是有控制的，如果开源能做到闭源的 95%，那如果闭源太贵，那完全就可以用开源来做，", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[99]/span"}, {"immutable": false, "orig_start": 0, "position_id": 929, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "如果开源和闭源能力差不多，那对闭源是一个很大的挑战。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[99]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 139, "global_sentence_id": 216, "edu_l1_label": "IOS"}, {"txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/13cv0e2Pf0TfPcF9w2hYH5g.png", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 428, "size": 0, "start": 0, "tags": ["img"], "tail": 0, "txt": "<img https://wcd-image-bucket-test.oss-cn-zhangjiakou.aliyuncs.com/raw/13cv0e2Pf0TfPcF9w2hYH5g.png", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/p[23]/span/img"}]}, "tags": ["img"], "label": "figure", "web_segment_id": 140, "global_sentence_id": 217, "edu_l1_label": "EDU_O"}, {"txt": "DeepSeek出圈的影响", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 931, "size": 0, "start": 0, "tags": ["h1"], "tail": 0, "txt": "DeepSeek出圈的影响", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/h1[25]/span"}]}, "tags": ["h1"], "label": "title1", "web_segment_id": 141, "global_sentence_id": 218, "edu_l1_label": "BOS"}, {"txt": "“比技术更重要的是愿景”", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 933, "size": 0, "start": 0, "tags": ["h1"], "tail": 0, "txt": "“比技术更重要的是愿景”", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/h1[26]/span"}]}, "tags": ["h1"], "label": "title1", "web_segment_id": 142, "global_sentence_id": 219, "edu_l1_label": "IOS"}, {"txt": "79. DeepSeek 的出圈让外界意识到了中国的 AI 很强。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 935, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "79. DeepSeek 的出圈让外界意识到了中国的 AI 很强。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[100]/span"}]}, "tags": [], "label": "content", "web_segment_id": 143, "global_sentence_id": 220, "edu_l1_label": "IOS"}, {"txt": "以前外界认为中国的 AI 进展落后美国两年，但 DeepSeek 表明其实差距在 3-9 个月，甚至某些方面更强。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 936, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "以前外界认为中国的 AI 进展落后美国两年，", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[100]/span"}, {"immutable": false, "orig_start": 0, "position_id": 938, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "但 DeepSeek 表明其实差距在 3-9 个月，甚至某些方面更强。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[100]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 143, "global_sentence_id": 221, "edu_l1_label": "IOS"}, {"txt": "80. 历史上中国被美国封锁的东西，如果能被突破的话最终都会很卷，AI 可能也是，DeepSeek能跑出来就是一个证明。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 940, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "80. 历史上中国被美国封锁的东西，如果能被突破的话最终都会很卷，AI 可能也是，DeepSeek能跑出来就是一个证明。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[101]/span"}]}, "tags": [], "label": "content", "web_segment_id": 144, "global_sentence_id": 222, "edu_l1_label": "IOS"}, {"txt": "81. DeepSeek 不是突然爆发的，这次 R1 结果很漂亮，触及到了美国从上到下的核心圈。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 942, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "81. DeepSeek 不是突然爆发的，", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[102]/span"}, {"immutable": false, "orig_start": 0, "position_id": 944, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "这次 R1 结果很漂亮，触及到了美国从上到下的核心圈。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[102]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 145, "global_sentence_id": 223, "edu_l1_label": "IOS"}, {"txt": "82. DeepSeek 是站在巨人的肩膀上，但探索前沿需要的时间和人力成本还是要高很多，R1 并不代表以后的训练成本会同时降低。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 946, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "82. DeepSeek 是站在巨人的肩膀上，但探索前沿需要的时间和人力成本还是要高很多，R1 并不代表以后的训练成本会同时降低。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[103]/span"}]}, "tags": [], "label": "content", "web_segment_id": 146, "global_sentence_id": 224, "edu_l1_label": "IOS"}, {"txt": "83. AI 探索者一定是需要更多算力的，中国作为追赶者可以发挥在工程能力上的优势。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 948, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "83. AI 探索者一定是需要更多算力的，中国作为追赶者可以发挥在工程能力上的优势。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[104]/span"}]}, "tags": [], "label": "content", "web_segment_id": 147, "global_sentence_id": 225, "edu_l1_label": "IOS"}, {"txt": "中国的大模型团队怎么用较少的算力做出成果，从而有一定的抵御能力、甚至做的更好，可能是未来中美 AI 格局的推演。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 950, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "中国的大模型团队怎么用较少的算力做出成果，从而有一定的抵御能力、甚至做的更好，可能是未来中美 AI 格局的推演。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[104]/span/strong"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 147, "global_sentence_id": 226, "edu_l1_label": "IOS"}, {"txt": "84. 中国今天还是在复现技术方案，reasoning 是 OpenAI 在 o1 提出的，所以接下来各个 AI labs 之间的差距在于谁能提出下一个 reasoning。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 952, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "84. 中国今天还是在复现技术方案，reasoning 是 OpenAI 在 o1 提出的，所以接下来各个 AI labs 之间的差距在于谁能提出下一个 reasoning。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[105]/span"}]}, "tags": [], "label": "content", "web_segment_id": 148, "global_sentence_id": 227, "edu_l1_label": "IOS"}, {"txt": "无限长度的 reasoning 可能是一个愿景。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 953, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "无限长度的 reasoning 可能是一个愿景。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[105]/span"}]}, "tags": [], "label": "content", "web_segment_id": 148, "global_sentence_id": 228, "edu_l1_label": "IOS"}, {"txt": "85. 不同 AI labs 的模型之间的核心差别在于 AI labs 本身的下一个愿景是什么，而不是技术。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 955, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "85. 不同 AI labs 的模型之间的核心差别在于 AI labs 本身的下一个愿景是什么，而不是技术。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[106]/strong/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 149, "global_sentence_id": 229, "edu_l1_label": "IOS"}, {"txt": "86. 毕竟，比技术更重要的是愿景。", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 957, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "86. 毕竟，比技术更重要的是愿景。", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[107]/strong/span"}]}, "tags": ["strong"], "label": "content", "web_segment_id": 150, "global_sentence_id": 230, "edu_l1_label": "IOS"}, {"txt": "（本文观点仅供行业研究参考，不作为投资依据）", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 959, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "（本文观点仅供行业研究参考，不作为投资依据）", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[108]/span"}]}, "tags": [], "label": "content", "web_segment_id": 151, "global_sentence_id": 231, "edu_l1_label": "EDU_O"}, {"txt": "推荐阅读", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 961, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "推", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[109]/section[2]/section/section/strong/span/strong/span"}, {"immutable": false, "orig_start": 0, "position_id": 963, "size": 0, "start": 0, "tags": ["strong"], "tail": 0, "txt": "荐阅读", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[109]/section[2]/section/section/strong/span/strong"}]}, "tags": ["strong"], "label": "O", "web_segment_id": 152, "global_sentence_id": 232, "edu_l1_label": "EDU_O"}, {"txt": "<img https://mmbiz.qpic.cn/sz_mmbiz_jpg/ow6przZuPIH6Nial5eWfUH2qqAiaF21kP2yojQicFXKrkz2MRXAKG4ICU5HPLJn5gnQX1z2JvXdzG6dPEiaLcbZMMg/640?wx_fmt=other&from=appmsg&wxfrom=5&wx_lazy=1&wx_co=1&tp=webp", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 538, "size": 0, "start": 0, "tags": ["img"], "tail": 0, "txt": "<img https://mmbiz.qpic.cn/sz_mmbiz_jpg/ow6przZuPIH6Nial5eWfUH2qqAiaF21kP2yojQicFXKrkz2MRXAKG4ICU5HPLJn5gnQX1z2JvXdzG6dPEiaLcbZMMg/640?wx_fmt=other&from=appmsg&wxfrom=5&wx_lazy=1&wx_co=1&tp=webp", "x": "/html/body/div[2]/div[2]/div[2]/div/div[1]/div[2]/section[109]/section[3]/section/section/p/img"}]}, "tags": ["img"], "label": "O", "web_segment_id": 153, "global_sentence_id": 233, "edu_l1_label": "EDU_O"}, {"txt": "                        腾讯科技                      ", "language": "chinese", "position": {"atoms": [{"immutable": false, "orig_start": 0, "position_id": 965, "size": 0, "start": 0, "tags": [], "tail": 0, "txt": "\n                        腾讯科技                      ", "x": "/html/body/div[4]/div[2]/div[2]/div[1]/div[1]/div/div/div/div/div[2]/div/div"}]}, "tags": [], "label": "O", "web_segment_id": 167, "global_sentence_id": 234, "edu_l1_label": "EDU_O"}], "type": "WEB"}
# 一场关于DeepSeek的高质量闭门会：比技术更重要的是愿景
## 神秘的DeepSeek“DeepSeek最重要的事是push智能”
## 探索者VS追赶者“AI类似阶跃函数，追赶者算力需求少10倍”
## 技术细节1：SFT“在推理层面不需要做SFT了”
## 技术细节2：数据“DeepSeek在数据标注上非常重视”
## 技术细节3：蒸馏“蒸馏坏处是模型diversity下降”
## 技术细节4：Process Reward“过程监督上限是人，结果监督才是模型上限”
## 其他公司为何没用DeepSeek方法？“大厂的模型得低调”
## 2025技术的分化与押注 “除Transformer能不能找别的架构”
## 开发者是否从闭源模型迁移至 DeepSeek？“目前还没有”
## OpenAI Stargate 500B叙事与算力需求变化
## 对二级市场的影响“短期情绪有压力，长期叙事继续”
## 开源 VS 闭源“如果能力差不多，对闭源是挑战”
## DeepSeek出圈的影响“比技术更重要的是愿景”