Neural networks often do not need to solve optimization problems exactly; instead, they minimize loss over a finite set of training examples, which is only an approximation of the larger problem.

The key lesson in machine learning is that you get what you optimize for; setting up a system to directly optimize for desired outcomes yields significantly better results than trying to combine unoptimized models.

While specialized models can be effective for specific tasks, the overall trend is towards general models that incorporate learnings from specialized iterations.

Removing human feedback from training may lead to models that are not grounded, as they would not learn from the actual consequences of their actions in the world.

To effectively remove facts from a model, it must be done at every stage of the training process, including pre-training and post-training adjustments.

The 'bitter lesson' in AI suggests that scalable training methods will prevail, emphasizing the importance of minimizing human biases in the learning process.

Pre-training is inherently broad and diverse, making it challenging to achieve the same breadth when using reinforcement learning in specific environments.

The goal in training a neural network for denoising images is to minimize the least squares error between the denoised and clean images across the training set.

You need big models to train smaller models effectively, and while we have efficient smaller models, they often rely on insights from larger models.