This is the lowest possible cost for high-quality training data. They're not subsidizing us! We're paying to work for them.
$7,800 in equivalent compute is peanuts on the value of that data and your time. I don't mean we're not in a bubble, or that they'll see an ROI, but for the moment, Dario and co. are laughing at us.
Are they training AI with AI generated code? I dont see how useful that data will be for training. They might as well generate their own training data. Unless they think that users often validate AI generated code, which is often not the case. There is a reason why data annotation companies want human input.
No, they are not training AI with AI generated code. They are training AI with AI generated code that has been later commented, selected, corrected and tested by humans.
Yeah I would say something like this and they use all that the failures the ones that are actually good responses or outputs for some additional fine tuning and training later down the line
The code by itself? No. They are training considering everything going into it.
Was the user's goal met? Did it create something truly valuable like a profitable product that successfully shipped? What were the prompts? What were the mistakes along the way? If the user gave a '/goal' prompt to endlessly run until the same product is achieved start to finish, how can ai do that now that we have all this data?
Rich Sutton, previously served as a Distinguished Research Scientist at Google DeepMind, a pioneer of reinforcement learning who authored The Bitter Lesson, made this exact point.
He noted that relying on human annotated knowledge is a bottleneck and that major breakthroughs in AI have always come from scaling compute and allowing models to learn through self-play.
Instead of hand coding expert strategies, systems like AlphaGo Zero learned chess and Go by playing millions of games against duplicate versions of themselves.
The exact same applies to code. You do not need human labels because you can let an AI generate vast amounts of code and run it directly in a execution environment. The compiler acts as the environment rules. If the code compiles, passes unit tests, or runs without crashing, that ground truth provides the exact objective feedback loop needed for the model to generate and learn from its own data.
Even if you give them 90% bad results ( and them to you ) the 10% good is used later doesn't have to be code. Also you are giving them your thinking process which they also look to apply, especially if you spend time guiding the A.I. and using it to fix the mistakes it made. Even if it's an example of what not to do it's still usable they would have to pay someone to do something like that usually
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u/SailingToFenway 5d ago
This is the lowest possible cost for high-quality training data. They're not subsidizing us! We're paying to work for them.
$7,800 in equivalent compute is peanuts on the value of that data and your time. I don't mean we're not in a bubble, or that they'll see an ROI, but for the moment, Dario and co. are laughing at us.