tehsauce

For those curious how it works, it’s essentially a script that plays the game but uses jev as a source of rng to make it stochastic

missing label 2025?

For the purpose of disclosure, it should say “Warning: AI generated code” in the commit message, not an advertisement for a specific product. You would never accept any of your other tools injecting themselves into a commit message like that.

“888 KiB Assistant” but the assistant itself is a multi terabyte rental-only model stored in some mysterious data center.

How does it do on gold stake?

Right, and when they compare to floating point accuracy they seem to be using the number of decimals supported by the mantissa, but the exponent is important no?

“A transformer predicts the next token”

Nope. A transformer is much more general than that. A GPT predicts the next token.

I was excited to try it out so I downloaded the repo and ran the build. However there were 100+ compilation errors. So I checked the commit history on github and saw that for at least several pages back all recent commits had failed in the CI. It was not clear which commit I should pick to get the semi-working version advertised.

I started looking in the Cargo.toml to at least get an idea how the project was constructed. I saw there that rather than being built from scratch as the post seemed to imply that almost every core component was simply pulled in from an open source library. quickjs engine, wgpu graphics, winit windowing & input, egui for ui, html parsing, the list goes on. On twitter their CEO explicitly stated that it uses a "custom js vm" which seemed particularly misleading / untrue to me.

Integrating all of these existing components is still super impressive for these models to do autonomously, so I'm just at a loss how to feel when it does something impressive but they then feel the need to misrepresent so much. I guess I just have a lot less respect and trust for the cursor leadership, but maybe a little relief knowing that soon I may just generate my own custom cursor!

I love this! Your results seem comparable to the counter strike or minecraft models from a bit ago with massively less compute and data. It's particularly cool that it uses real world data. I've been wanting to do something like this for a while, like capturing a large dataset while backpacking in the cascades :)

I didn't see it in an obvious place on your github, do you have any plans to open source the training code?

There has been some good research published on this topic of how RLHF, ie aligning to human preferences easily introduces mode collapse and bias into models. For example, with a prompt like: "Choose a random number", the base pretrained model can give relatively random answers, but after fine tuning to produce responses humans like, they become very biased towards responding with numbers like "7" or "42".

It's impossible to beat with random actions or brute force, but you can get surprisingly far. It doesn't take too long to get halfway through route 1, but even with insane compute you'll never make it even to viridian forest.