全球掀DeepSeek复现狂潮！硅谷巨头神话崩塌，30刀见证啊哈时刻_老虎社区_美港股上老虎

全球掀DeepSeek复现狂潮！硅谷巨头神话崩塌，30刀见证啊哈时刻

新智元2025-01-26

这些天，硅谷彻底处于中国公司带来的大地震余波中。全美都在恐慌：是否全球人工智能的中心已经转移到了中国？就在这当口，全球复现DeepSeek的一波狂潮也来了。诚如LeCun所言：「这一次，正是开源对闭源的胜利！」在没有顶级芯片的情况下，以极低成本芯片训出突破性模型的DeepSeek，或将威胁到美国的AI霸权。大模型比拼的不再是动辄千万亿美元的算力战。OpenAI、Meta、谷歌这些大公司引以为傲的...

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sedf123582
·2025-01-27
就算粪坑里真的长出了鲜花，最后也只能变成化肥
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3
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MOORE
·2025-01-26
老虎内部有否党组织？
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3
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金刚狼割韭菜
·2025-01-26
该AI模型适合建立人类历史第一个AI党支部
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3
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零度可口不可乐
·2025-01-26
评论区太经典了
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5
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LUO8888
·2025-01-26
能不能讲人话？我们不是研究人员。
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2
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SanTi小汤
·2025-01-26
搜索的确实很全面，我自己都不知道的很多媒体转发我的文章被它都整理出来了[捂脸] [捂脸] [捂脸] 但是总结性和自我分析性还需要进步。目前主要是摘抄现成内容整理为主，不过比百度AI强太多了
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2
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与韭A不共戴天
·2025-01-26
又弯道超车了是吧？赢麻
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5
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tiger20250101
·2025-01-26
看股价，不要看人吹，要是英伟达股价砍一半，我就信你说的
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7
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- tiger20250101:
  会大幅减少 gpu 需求
  2025-01-27
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- wuxxx:
  deepseek也是基于nv的gpu训练出来的呀😂，而且开源模型没办法形成有效的护城河，不出一个月将会有大量的公司从这次事件中吸取经验
  2025-01-27
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antonyye
·2025-01-26
自嗨...。
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1
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helloA
·2025-01-26
该封禁的还是封禁，该不让说的还是不让说。妥妥的AI界的工具人
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2
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HunterRen
·2025-01-26
核电股要暴跌了
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点赞
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- AREFA:
  gpt又没有来源，怎么基于
  2025-01-27
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- 敲门行动回复AREFA:
  不是说他的计算结果是基于chatgp吗
  2025-01-26
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- AREFA:
  因为预期算力所需要的巨大能源缺口被证伪了
  2025-01-26
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查看更多 1 条评论
zack_zuti
·2025-01-26
垃圾新闻
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The myth of Silicon Valley giants collapses, 30 knives witness the aha moment","themeId":"74d6f673084f3f5c2c7f8a132abc3f6a","isJumpTheme":false,"ttsUrl":"https://static.tigerbbs.com/470328a8955295def6466de1b88dd86f","symbols_score_info":{},"content_text":"这些天，硅谷彻底处于中国公司带来的大地震余波中。全美都在恐慌：是否全球人工智能的中心已经转移到了中国？就在这当口，全球复现DeepSeek的一波狂潮也来了。诚如LeCun所言：「这一次，正是开源对闭源的胜利！」在没有顶级芯片的情况下，以极低成本芯片训出突破性模型的DeepSeek，或将威胁到美国的AI霸权。大模型比拼的不再是动辄千万亿美元的算力战。OpenAI、Meta、谷歌这些大公司引以为傲的技术优势和高估值将会瓦解，英伟达的股价将开始动摇。种种这些观点和讨论，让人不禁怀疑：数百亿美元支出，对这个行业真的必要吗？甚至有人说，中国量化基金的一群天才，将导致纳斯达克崩盘。从此，大模型时代很可能会进入一个分水岭：超强性能的模型不再独属于算力巨头，而是属于每个人。30美金，就能看到「啊哈」时刻来自UC伯克利博士生潘家怡和另两位研究人员，在CountDown游戏中复现了DeepSeek R1-Zero。他们表示，结果相当出色！实验中，团队验证了通过强化学习RL，3B的基础语言模型也能够自我验证和搜索。更令人兴奋的是，成本不到30美金（约217元），就可以亲眼见证「啊哈」时刻。这个项目叫做TinyZero，采用了R1-Zero算法——给定一个基础语言模型、提示和真实奖励信号，运行强化学习。然后，团队将其应用在CountDown游戏中（这是一个玩家使用基础算术运算，将数字组合以达到目标数字的游戏）。模型从最初的简单输出开始，逐步进化出自我纠正和搜索的策略。在以下示例中，模型提出了解决方案，自我验证，并反复纠正，直到解决问题为止。在消融实验中，研究人员运行了Qwen-2.5-Base（0.5B、1.5B、3B、7B四种参数规模）。结果发现，0.5B模型仅仅是猜测一个解决方案然后停止。而从1.5B开始，模型学会了搜索、自我验证和修正其解决方案，从而能够获得更高的分数。他们认为，在这个过程，基础模型的是性能的关键。他们还验证了，额外的指令微调（SFT）并非是必要的，这也印证了R1-Zero的设计决策。这是首个验证LLM推理能力的实现可以纯粹通过RL，无需监督微调的开源研究基础模型和指令模型两者区别：指令模型运行速度快，但最终表现与基础模型相当指令输出的模型更具结构性和可读性此外，他们还发现，具体的RL算法并不重要。PPO、GRPO、PRIME这些算法中，长思维链（Long CoT）都能够涌现，且带来不错的性能表现。而且，模型在推理行为中非常依赖于具体的任务：对于Countdow任务，模型学习进行搜索和自我验证对于数字乘法任务，模型反而学习使用分布规则分解问题，并逐步解决苹果机器学习科学家Yizhe Zhang对此表示，太酷了，小到1.5B的模型，也能通过RL涌现出自我验证的能力。7B模型复刻，结果令人惊讶港科大助理教授何俊贤的团队（共同一作黄裕振、Weihao Zeng），只用了8K个样本，就在7B模型上复刻出了DeepSeek-R1-Zero和DeepSeek-R1的训练。结果令人惊喜——模型在复杂的数学推理上取得了十分强劲结果。项目地址：https://github.com/hkust-nlp/simpleRL-reason他们以Qwen2.5-Math-7B（基础模型）为起点，直接对其进行强化学习。整个过程中，没有进行监督微调（SFT），也没有使用奖励模型。最终，模型在AIME基准上实现了33.3%的准确率，在AMC上为62.5%，在MATH上为77.2%。这一表现不仅超越了Qwen2.5-Math-7B-Instruct，并且还可以和使用超过50倍数据量和更复杂组件的PRIME和rStar-MATH相媲美！其中，Qwen2.5-7B-SimpleRL-Zero是在Qwen2.5-Math-7B基础模型上仅使用纯PPO方法训练的，仅采用了MATH数据集中的8K样本。Qwen2.5-7B-SimpleRL则首先通过Long CoT监督微调（SFT）作为冷启动，然后再进行强化学习。在这两种方法中，团队都只使用了相同的8K MATH样本，仅此而已。大概在第44步的时候，「啊哈时刻」出现了！模型的响应中，出现了自我反思。并且，在这个过程中，模型还显现了更长的CoT推理能力和自我反思能力。在博客中，研究者详细剖析了实验设置，以及在这个强化学习训练过程中所观察到的现象，例如长链式思考（CoT）和自我反思机制的自发形成。与DeepSeek R1类似，研究者的强化学习方案极其简单，没有使用奖励模型或MCTS（蒙特卡洛树搜索）类技术。他们使用的是PPO算法，并采用基于规则的奖励函数，根据生成输出的格式和正确性分配奖励：如果输出以指定格式提供最终答案且正确，获得+1的奖励如果输出提供最终答案但不正确，奖励设为-0.5如果输出未能提供最终答案，奖励设为-1该实现基于OpenRLHF。初步试验表明，这个奖励函数有助于策略模型快速收敛，产生符合期望格式的输出。第一部分：SimpleRL-Zero（从头开始的强化学习）接下来，研究者为我们分享了训练过程动态分析和一些有趣的涌现模式。训练过程动态分析如下所示，所有基准测试的准确率在训练过程中都在稳步提高，而输出长度则呈现先减少后逐渐增加的趋势。经过进一步调查，研究者发现，Qwen2.5-Math-7B基础模型在初始阶段倾向于生成大量代码，这可能源于模型原始训练数据的分布特征。输出长度的首次下降，是因为强化学习训练逐渐消除了这种代码生成模式，转而学会使用自然语言进行推理。随后，生成长度开始再次增加，此时出现了自我反思机制。训练奖励和输出长度基准测试准确率（pass@1）和输出长度自我反思机制的涌现在训练到第 40 步左右时，研究者观察到：模型开始形成自我反思模式，这正是DeepSeek-R1论文中所描述的「aha moment」（顿悟时刻）。第二部分：SimpleRL（基于模仿预热的强化学习）如前所述，研究者在进行强化学习之前，先进行了long CoT SFT预热，使用了8,000个从QwQ-32B-Preview中提取的MATH示例响应作为SFT数据集。这种冷启动的潜在优势在于：模型在开始强化学习时已具备long CoT思维模式和自我反思能力，从而可能在强化学习阶段实现更快更好的学习效果。与RL训练前的模型（Qwen2.5-Math-7B-Base + 8K 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