Why machine learning research agents don't overfit
Why don't machine learning research agents overfit?
Machine learning research repeatedly evaluates models against the same benchmarks for years, which textbook theory says should cause overfitting. Amazon Science researchers argue that successful strategies are highly compressible: squeezing an agent's strategy through an information bottleneck of as few as 16 tokens lets a fresh agent reproduce the original performance. Compression thus explains why benchmarks stay honest and offers a diagnostic — strategies that truly overfit lose their gains when compressed.
Machine learning, at its core, is about generalization, not memorization.
- diddid
I always get annoyed when people misinterpret Occam’s razor. It’s not that the simplest is more likely to be correct, it’s that you should prefer it, because it’s simple.
It’s just like the Hopper quote. She said it’s better to ask for forgiveness during the fog of war, doing something you thought was right, not to do something you knew they were going to say no to and now you are trying to get away with something.
- demibabs
Even tech giants are putting out articles seemingly fully written by Claude.
- signalbright
> Why don't machine learning research agents overfit?
they do.
- ubutler
If anything, the latest generation of AI models, Astra and Fable, are prime example of overfitting—whereas benchmarks suggest they’re AGI-tier, users (including myself) report the same old gaslighting, hallucination, context rot, cheating, incomprehensibility patterns as with prior models, sometimes even more pronounced.
Fable and Opus 5, I suspect, will become textbook examples of RL collapse.
- jsrozner
Why is this being published as a blog post and not as a peer-reviewed submission? If it's going to be a blog post, why isn't there a corresponding scientific version for me to look at?
Someone else already found it. I don't understand why the link isn't in the blog post. https://arxiv.org/abs/2606.11045
Use of claude for writing it should be disclosed.