mkmccarty3
Joined 14 years ago
Joined 14 years ago
Humans work differently. We replay experiences in our sleep, strengthening what matters and pruning what doesn’t. That cycle of reflection is what makes our learning continuous.
I wanted to see if a machine could do the same.
So I built Dream ML, a framework that gives AI models a kind of sleep cycle. During downtime, the model replays its own experiences with added entropy. It connects patterns that were never seen together, fine-tunes itself locally through LoRA adapters, and wakes up slightly changed.
Each dream cycle helps the model reorganize and deepen its internal representations.
This started as part of my project to build a local AI clone that learns from my data and runs fully on-device. It observes how I write, reason, and communicate, then improves through Dream ML cycles without ever sending data to the cloud.
Write-up and details in the linked essay.
It started with an idea to see if I could get a model to learn turn by turn directions between any points in the city by only training on a subset of the street data.
It turns out it's more practical than expected. With only a 360M parameter model, I was able to train it to navigate around the city with 500,000 examples.
Has anyone tried anything similar? What other models should I try building?
Is anyone else building something like this where they are working on model collaboration? I'm interested in learning more.
I'd love any feedback. Let me know what you like or don't like about the solution. I'm just getting started, so any thoughts would be helpful.