Modern AI is still directly dependent on human labeling and human data in general. And there are a lot of problems with this: it's expensive, time-consuming, "data runs out", etc.
🟢 What META done?
They are also convinced that this is essentially a hard ceiling on the path to AGI: if you only train agents on human traces, then the learning boils down to refining human experience. So can we be 100% sure that such systems can learn something outside the distribution and become smarter than us? This is especially true for areas such as coding, which will be discussed further.
The researchers proposed Self-Play SWE-RL - a way to train agents so that they can self-improve on their own data.
Not a breakthrough, but few pipelines today give such significant increases, so it's quite interesting (but code wasn't provided).
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