Comment by programjames

If there isn't a path humans know how to take with their current technology, it isn't a solved problem. It's much different than people training an image model for research purposes, and knowing that $100m in compute is probably enough for a basic video model.

Large latent flow models are unbiased. On the other hand, if you purely use policy optimization, RLHF will be biased towards short horizons. If you add in a value network, the value has some bias (e.g. MSE loss on the value --> Gaussian bias). Also, most RL has some adversarial loss (how do you train your preference network?), which makes the loss landscape fractal which SGD smooths incorrectly. So, basically, there's a lot of biases that show up in RL training which can make it both hard to train, and even if successful, not necessarily optimizing what you want.

A good start would be:

1. Robust to adversarial attacks (e.g. in classification models or LLM steering).

2. Solving ARC-AGI.

Current models are optimized to solve the current problem they're presented, not really find the most general problem-solving techniques.

That has been solved with RAG, OCR-ish image encoding (deepseek recently) and just long context windows in general.