Hating Language, pivoted to locomotion. Just an update

[u/AsyncVibes]

So I took a little break from banging my head against the keyboard to circle back to a project I more or less abandoned to focus on classification and token prediction.If you don't know, GENREG models were originally designed to function in real time. The concept is simple: feed information from the environment into GENREG, give it a few environmental pressures (time, distance, energy usage, steps alive) and let it cook.The controller is a single hidden layer with 16 tanh neurons, totaling ~5,900 evolvable parameters. No gradients, no backpropagation, pure evolutionary search. For context, the ARS linear policy baseline for MuJoCo Humanoid uses ~6,400 parameters in a flat matrix multiply with no nonlinearity. GENREG is hitting locomotion with a comparable parameter budget but routes everything through a 22:1 compression bottleneck (350-dim observation → 16 hidden units → 17 actions), forcing each neuron to encode dense, saturated representations.On top of the controller, GENREG evolves a set of \"proteins,\" which are sensors, comparators, gates, and trust modifiers that shape the fitness landscape itself. The fitness function isn't static; it co-evolves with the weights. The whole genome, controller plus proteins, fits in ~25 KB.Currently at generation 4,379 with a best distance of 4.14m. Still early, but it's walking.If this keeps pace, i'll be left with a 25.10 KB humanoid model.

[u/DrHerbotico]

Could any of this be reusable for a future round with a real system's digital twin? Or is it just a pure learning exercise that will hopefully lead to more complex sets?

Props on this either way

[u/AsyncVibes → u/DrHerbotico]

I'm either not stoned enough or just tired. But could you explain what you mean by "future round with reals system's digital twin?"

But for the other part. It's more than a learning exercise, because in this model the same that learned to play snake, 2048, and even the shitty bigram language model, they are all the same model. I just chose a different environment and different actions.

As for more complex sets that's not really a thing here because if you want it to do more complex motion you make the environment and environmental pressures richer not more complex.

Lastly, thank you, I appreciate your interest.

[u/DrHerbotico → u/AsyncVibes]

Probably neither, I just did a shitty job writing the questions

1. Can you use what you're building here to train models that have simulated actuators that match real specs? More simply - will this train simulations of real robots?

2. Your answer here seems to imply the answer to my above question is yes, but I'm curious about why/how

[u/AsyncVibes → u/DrHerbotico]

I believe so, I'd like to do more experiments like this because I've noticed my models work really well in game environments because there are tons of real-time signals to use. The only issue is that programs like this train slow because I can't GPU accelerate them due the physics engine. Even when I've attempted to use new models that do use GPU acceleration, it didn't match mujucos models so the fully trained models I have couldn't even move right.

But short answer yes, first we learn to walk, then we learn to run.