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.

Why don't machine learning research agents overfit?

they do.

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.

Do the submitters really not notice that this is AI slop? Do they like this? It is a complete pain to read.

Fable and Opus 5, I suspect, will become textbook examples of RL collapse.

Yes, I'm familiar with keystone results such as Solomonoff induction. It's a direct counterexample to compression - your intensional algorithm can completely outrun reality. I can literally specify a huge mega-algorithm that just searches over all possible Turing machines and evaluates them, and it's an optimal compressor. It's completely vacuous though. You can always hide the "heavy work" in your mappings and descriptions. It's ironic that a kolomogorov complexity minimizer is so loaded that it's vacuous.

This is pretty much why I roll my eyes at this point at all the compression is intelligence memes.

I wonder when intervention and causality will hit the mainstream. These tools were designed specifically to counteract purely predictive theories. But your average compression dude will hold tight to their paradigms and slogans, not realize their internal contradictions (that their own field has brought up), and then whenever a new paradigm suddenly becomes visible and mainstream, they'll latch onto that. It's not principled at all.

And to be clear - I do think intelligence is some amount of compression, and I am well aware of formal results such as the arithmetic decoding theoretical and empricial result. Just annoyed. It's literally no different than the whole Bayesianism meme. If you're not actually practicing that type of intelligence as a basis, then you don't get to go around beating the drum about how it's the ultimate reality. You're just spouting dogma to feel like part of an in-group.

We need to retvrn to rss feeds

Machine learning, at its core, is about generalization, not memorization.

Well they memorize the patterns.

memorization doesnt mean rote learning.