I find the entire field lacking when it comes to long-horizon problems. Our current, widely used solution is to scale, but we're nowhere near achieving the horizon scales even small mammal brains can handle. Our models can have trillions of parameters, yet a mouse brain would still outperform them on long-horizon tasks and efficiency. It's something small, simple, and elegant—an incredible search algorithm that not only finds near-optimal routes but also continuously learns on a fixed computational budget.

I'm honestly a bit envious of future engineers who will be tackling these kinds of problems with a 100-line Jupyter notebook on a laptop years from now. If we discovered the right method or algorithm for these long-horizon problems, a 2B-parameter model might even outperform current models on everything except short, extreme reasoning problems.

The only solution I've ever considered for this is expanding a model's dimensionality over time, rather than focusing on perfect weights. The higher dimensionality you can provide to a model, the greater its theoretical storage capacity. This could resemble a two-layer model—one layer acting as a superposition of multiple ideal points, and the other layer knowing how to use them.

When you think about the loss landscape, imagine it with many minima for a given task. If we could create a method that navigates these minima by reconfiguring the model when needed, we could theoretically develop a single model with near-infinite local minima—and therefore, higher-dimensional memory. This may sound wild, but consider the fact that the human brain potentially creates and disconnects thousands of new connections in a single day. Could it be that these connections steer our internal loss landscape between different minima we need throughout the day?