开源不等于开放权重：Gary Marcus 辨析概念
Open-source is NOT the same as open-weight
It’s no exaggeration to say that open-source software — in which the complete source code is available to anyone — has changed the world and accelerated software development.
毫不夸张地说，开源软件——即完整源代码对任何人开放——已经改变了世界，并加速了软件开发。
But open-source and open-weight are two very different things, and too many people —this morning it was editors and writers at The New York Times among others—are failing to distinguish the two.
但开源和开放权重是两件截然不同的事情，太多人——今天早上包括《纽约时报》的编辑和记者等——未能区分这两者。
The essence of open-source is twofold: transparency and customizability. If you want to know how something works, you look at the code. You can change literally anything about that code (“forking” a new version of that code, for example, if you want to make a different version). All of this is fantastic for the community, and that is why open-source has been such a dynamic force. It’s called open-source because you release the source code of the final product. Open-weight doesn’t do that.
开源的实质是双重的：透明性和可定制性。如果你想知道某物如何工作，你可以查看代码。你可以对代码进行任何修改（例如，如果你想制作一个不同的版本，可以“分叉”该代码的新版本）。所有这些对社区来说都是极好的，这就是为什么开源一直是如此强大的力量。它被称为开源，因为你发布了最终产品的源代码。开放权重则不是这样。
Open-weight piggybacks onto all the great press that open-source has but offers few of the advantages. Open-weight models are NOT fully customizable, and not transparent. They are the product of machine learning (the weights of a trained neural network), but not the whole thing, from soup to nuts. That means you can run them (or “posttrain” them), but, crucially, users can’t customize or improve them to remotely the same degree (more about that in a second), and outsiders can’t investigate them to nearly the same degree. You also can’t build community in the same way.
开放权重借用了开源所获得的所有好评，但提供的优势却很少。开放权重模型并非完全可定制，也不透明。它们是机器学习（训练好的神经网络的权重）的产物，但不是从头到尾的全部。这意味着你可以运行它们（或“后训练”它们），但关键是，用户无法像开源那样进行定制或改进（稍后详述），外部人员也无法进行同等程度的调查。你也不能以同样的方式建立社区。
If pigeons are “just rats with better PR”, open-weights is open-source with all the good press but far fewer advantages.
如果鸽子是“公关更好的老鼠”，那么开放权重就是拥有所有好评但优势少得多的开源。
Understanding all this requires a basic familiarity with the life cycle of a machine learning model. Let’s start with a simplified sketch I have snarfed (and modified) from geeksforgeeks.org. The highlighting, which I will explain in a moment, is mine.
理解这一切需要对机器学习模型的生命周期有基本的了解。让我们从我从geeksforgeeks.org上抓取（并修改）的一个简化示意图开始。高亮部分是我添加的，稍后我会解释。
(Not shown is “post training”, in which people try to align and bend trained “base” models to their will. That’s all you can do with an open-weight model; I will give you examples of what you can’t do in a moment.)
（未显示的是“后训练”，即人们试图对齐和调整训练好的“基础”模型以符合他们的意愿。这就是你能对开放权重模型所做的一切；稍后我会给你一些你不能做的例子。）
When someone releases an open-weight model they are releasing the (highlighted) candidate model [also sometimes known as a base model]—which is the output of a complex process—not the full pipeline that generated that model. You can adapt that candidate model, but you can’t rebuild it or investigate it to your liking, the way you could with a true open-source model, such as AllenAI’s Olmo or Nvidia’s Nemotron, which truly is what it says on the tin: “open weights, training data, and recipes.”
当某人发布一个开放权重模型时，他们发布的是（高亮显示的）候选模型[有时也称为基础模型]——这是一个复杂过程的输出——而不是生成该模型的完整流程。你可以调整该候选模型，但你不能像对待真正的开源模型那样，随心所欲地重建它或对其进行调查，例如AllenAI的Olmo或Nvidia的Nemotron，它们名副其实：“开放权重、训练数据和配方”。
In contrast, when people release open-weight models, they are not revealing the Full Monty. They are not releasing the raw data; they are not revealing how they preprocessed that data; and they are not revealing the exact algorithms and parameters that they used in training. (For those who can stand yet one more loose metaphor, it’s like they are releasing a cake, but not the recipe; you can add icing on top, and maybe some fruit or hot fudge too, but you can’t change the raw ingredients that went into the cake itself.)
相比之下，当人们发布开放权重模型时，他们并没有展示全部细节。他们没有发布原始数据；他们没有透露他们如何预处理这些数据；他们也没有透露他们在训练中使用的确切算法和参数。（对于那些还能忍受再多一个松散比喻的人，这就像他们发布了一个蛋糕，但没有配方；你可以在上面加糖霜，也许还可以加些水果或热软糖，但你不能改变蛋糕本身的原始成分。）
All this makes a world of difference, to developers who might use the systems, to regulators, to scientists, and ultimately to the world.
所有这些都产生了巨大的差异，对可能使用这些系统的开发者、对监管者、对科学家，最终对世界都是如此。
For developers, let’s say you have a hypothesis that you could make a safer AI if you delete all of the Reddit data from training. Sorry, out of luck: in an open-weight system (as opposed to a truly open-source system) you can’t change the training data at all, and in fact you can’t even see the training data. Or let’s say you would like to train a model on legal reasoning and recognize that common law and statutory law are different. To reduce confusion in the system you would like to filter out the common law stuff before training; again you are out of luck. Or suppose you would like to use Big Company’s training process purely on your own data, rather than on whatever random stuff they scraped from the web. Nope, can’t do that either. (Nor can you share the results of any of those experiments with the community, since you can’t run them in the first place.)
对于开发者来说，假设你有一个假设，如果你从训练中删除所有Reddit数据，你就能制造出更安全的AI。抱歉，运气不好：在开放权重系统（相对于真正的开源系统）中，你根本无法改变训练数据，事实上你甚至看不到训练数据。或者假设你想训练一个关于法律推理的模型，并认识到普通法和成文法是不同的。为了减少系统中的混淆，你想在训练前过滤掉普通法的内容；同样，你运气不好。或者假设你想使用大公司的训练过程，但只使用你自己的数据，而不是他们从网上抓取的任何随机内容。不，那也不行。（你也不能与社区分享任何这些实验的结果，因为你根本无法运行它们。）
Or say you are a regulator, and you would like to know how much bias there is towards white males in the training data. Sorry, can’t look at that, either. What kinds of instructions are there relevant to bioweapons? Again, nope.
或者假设你是一名监管者，你想知道训练数据中对白人男性的偏见有多大。抱歉，那也不能看。有哪些与生物武器相关的指令？同样，不能看。
Are you a scientist? Want to investigate how much of what the model does is regurgitation and how much extends beyond the training data, which is arguably the central question in AI? Sorry, out of luck there, too. Those who released the open-weight model could do that, since they are privy to all these details that are actually closed, but you can’t. Or maybe you just want to know whether the answers to your favorite benchmarks are actually already in the training data? Nope, can’t look at that, either.
你是科学家吗？想研究模型的行为有多少是复读训练数据，又有多少超越了训练数据——这可以说是人工智能领域的核心问题？抱歉，你也没戏。发布开放权重模型的公司本可以做到这一点，因为他们掌握着这些实际上并不公开的细节，但你不行。或者你只是想知道你最喜欢的基准测试的答案是否已经在训练数据里了？不行，那个你也看不了。
All of this is unfortunate. It means that developers often can’t really use the open-weight models (despite the hype) to develop what they want; scientists can’t use them to test a lot of key hypotheses, and regulators can’t peer inside the process.
这一切都很不幸。这意味着开发者往往不能真正利用开放权重模型（尽管宣传得天花乱坠）来开发他们想要的东西；科学家不能用它们来检验许多关键假设，监管者也无法窥探其内部过程。
Furthermore, if you wanted to know how much of the training data was copyrighted, you can’t look at that either — which is perhaps part of why none of the big companies want to release true open-source, and instead settle for open-weight, getting nearly all of the positive press without actually helping the community nearly as much.
此外，如果你想知道训练数据中有多少是受版权保护的，你也无法查看——这或许正是为什么没有一家大公司愿意发布真正的开源模型，而是满足于开放权重，既获得了几乎所有的正面报道，又没有真正帮到社区多少。
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What prompted me to finally write about this?
是什么促使我终于写下这篇文章？
Meta just released an open-weight model. To his credit, Zuckerberg himself got the terminology right here:
Meta 刚刚发布了一个开放权重模型。值得称赞的是，扎克伯格本人在这里用词准确：
But the Times did not:
但《纽约时报》却没有：
Putting scares quotes around open-source does not make an open-weight model more open. And although one can post-train1 Meta’s new model, readapting it to some degree, for all the reasons I just described, it can’t be freely modified, nor used for proper science. You can’t do any of the modifications I described above. And you can’t figure out how the model got to be the way it is in the first place.
给“开源”加上引号并不会让开放权重模型变得更开放。尽管你可以对 Meta 的新模型进行后训练，在一定程度上重新调整它，但出于我上面描述的所有原因，它不能被自由修改，也不能用于真正的科学研究。你不能进行我上面描述的任何修改。你也无法弄清楚模型最初是如何变成现在这个样子的。
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Zuckerberg knows perfectly well the difference between open-weight and open-source. A few weeks ago he was extolling the value of open-source:
扎克伯格非常清楚开放权重和开源之间的区别。几周前，他还在赞美开源的价值：
But also played bait and switch with another tweet here:
但又在另一条推文中玩起了偷换概念的把戏：
And then today he released a model that is NOT open-source, hoping you wouldn’t notice the difference between the two. And, sadly, the Times didn’t.
然后今天他发布了一个并非开源的模型，希望你不会注意到两者之间的区别。可悲的是，《纽约时报》确实没注意到。
I implore them, and everyone else, for the love of Turing, please don’t confuse open-weight with open-source.
我恳求他们，也恳求所有人，看在图灵的面子上，请不要再把开放权重和开源混为一谈了。
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P.S. Bonus analogy; pick your favorite:
附言：额外打个比方；选一个你喜欢的：
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Post-training is a tiny bit like adopting an adult stray dog and trying to retrain it; it’s not that you can’t do anything, but it’s simply not the same as training it from day one.
后训练有点像收养一只成年流浪狗并试图重新训练它；并不是说你什么都不能做，但就是和从第一天开始训练不一样。
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力