I see your point that our brain both evaluates our actions and produces new actions. The idea of extrinsic reward is odd.

I wonder at a practical level what sota systems have a coupled backbone and then an action head and a reward head.

Happy to view this as a north star, but I'm curious.

Relatedly: maybe also a good time to question our assumptions about why peer review is helpful in the first place.

www.experimental-history.com/p/the-rise-a...

But in the future, it could look like

```python
from tap import Tap, Positional, ShortFlag, AddArgKwargs
from typing import Annotated

class Args(Tap):
arg_1: Annotated[int, Positional]
arg_2: Annotated[list[str], ShortFlag("a"), AddArgKwargs(nargs=2)]
```

For example, passing arguments currently looks like

```python
from tap import Tap

class Args(Tap):
arg_1: int
arg_2: list[str]

def configure(self) -> None:
self.add_argument("arg_1")
self.add_argument("-a", "--arg_2", nargs=2)
```

I believe these techniques can lead to:
new simulators that enable optimization through contact
new probabilistic algorithms with discontinuities
new differentiable renderers that better model occlusion
new ways to solve ML problems e.g. selecting MOEs and doing image gen in continuous space

Full abstract
www.eecs.mit.edu/eecs-events/...

TLDR: Modeling discontinuities (occlusion, cracks, and contact) is hard, but distribution theory gives a systematic way to model these phenomena. I introduce distribution programming, which enables implementation/autodiff by using programming primitives that denote distributions.

Manual, light-weight intuition building, automation to get a robust setup that can easily be edited, and manually edit.