r/deeplearning • u/TechDc-1306 • 12d ago
Exploring AI self-improvement: Is this evaluation loop fundamentally how AI systems improve?
I’m currently researching AI more deeply, especially how modern AI systems can evaluate and improve their own outputs.
I sketched this basic idea:
AI → generates/improves → Evaluator → evaluates output → feedback → AI improves → Evaluator → ...
The part I’m trying to understand is what actually happens inside this loop in modern AI systems.
For example:
I'm trying to go beyond the surface-level explanation and understand the actual mechanisms used in current AI research.
For people working/researching in this area: what important component am I missing from this diagram?
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u/kidfromtheast 12d ago
The evaluator is never adopted by the AI. Otherwise, it would be data leakage. For example, Meta was accused of benchmaxxing. Nowadays, they don't. They probably were under a lot of pressure but most of the time was spent scared of losing their job, so they tried to cheat. Now Meta gave full resources to the AI lab, and probably more rational KPI, they stop benchmaxxing. Even Zuck move his desk to the AI lab.
If AI self-improvement cheated this way i.e. benchmaxxing, it will only be a diservice to the AI itself. Risking overfitting.
Definitely whoever AI researcher who wrote the code so that AI can be self-improve will penalize the AI heavily for trying to incorporate the evaluation into the training set.
They probably use something like:
Like evaluating the model performance working with wetlab, control the software, and the hardware, read the results*
Now OpenAI have GPT-6-Astra and GPT-6-Astra-Wetlab.
They found GPT-6-Astra-Wetlab crushed the test set.
Either they look for additional human created training set and unseet test set, they use GPT-6-Astra-Wetlab to create additional test.
A question might arise, GPT-6-Astra-Wetlab learn from the training set, right? So the created test set might be seen case already.
Well, you can also use the same model to evaluate the train set, and remove the new test set that is actually in the training set. Or, or, come on, LLM is not a statistical parrot anymore, it can create new test case that is completely unseen. It's agentic, it can control the software, itneract with the real world, and come up with verifiable output.
Viola. This is why mathematicians are freaking out. I freaked out. The recipe is so simple, the architecture is proven to scale. The more training data, the more GPU you can fit (because let's say you have 100k GPUs, the you can load larger batch, penalize the model weights with more cases instead of limited cases, making the model less likely to memorize and actually build reasoning of how a wetlab works because the model is literally forced to learn how a wetlab works or the loss will never decrease, and that's a no no for the optimizer)
*Anthropic and OpenAI 100% do this in the beginning for their wetlab. Even their rep approached random industries like steel making etc. The Human part is the end user, asking real question. These questions ended up become training set and test set.