Outcome-Based Reinforcement Learning to Predict the Future (opens in new tab)

(arxiv.org)

99 pointsbturtel10mo ago15 comments

15 comments

ctoth10mo ago

Do you want paperclips? Because this is how you get paperclips!

Eliminate all agents, all sources of change, all complexity - anything that could introduce unpredictability, and it suddenly becomes far easier to predict the future, no?

JoshTriplett10mo ago

> Do you want paperclips? Because this is how you get paperclips!

Don't^W worry, there are many other ways of getting paperclips, and we're doing all of them.

sitkack10mo ago

Even explaining how not to get paper clips, gets you paper clips when you can invert the loss function. Paper clips for everyone!

vlovich12310mo ago

I don't know. Paperclips are awful useful. Would it be so bad to build more of them?

throwaway7127110mo ago

https://www.decisionproblem.com/paperclips/index2.html go ahead :)

Ygg210mo ago

That's all fun and games until paperclip maximizers starts looking at your blood as source of iron.

valine10mo ago

So instead of next token prediction its next event prediction. At some point this just loops around and we're back to teaching models to predict the next token in the sequence.

lumost10mo ago

Tokens are an awfully convenient way to describe an event.

phyalow10mo ago

Tokens are just discretized state representations.

ww52010mo ago

It’s the next state. So instead of spitting out words, it will spit out a whole movie, or a sequence of world states in a game or simulation.

jldugger10mo ago

From the abstract

> A simple trading rule turns this calibration edge into $127 of hypothetical profit versus $92 for o1 (p = 0.037).

I'm lazy: is this hypothetical shooting fish in a barrel, or is it a real edge?

nyrikki10mo ago

Note the 'hypothetical profit' part , I know of several groups looking for opportunities to skim off LLM traders, leveraging its limited sensitivity, expressiveness, and the loss of tail data.

Predictive AI is problematic no matter what tool you use. Great at demoware that doesn't deliver.

I am sure there are use cases, but it would be augmentation, not a reliable approach by itself.

amelius10mo ago

Why would you use RL if you're not going to control the environment, but just predict it?

TOMDM10mo ago

Because they're training a predictor, not an agent?

garbagecoder10mo ago

"a couple of wavy lines"

bzzzzz "sorry this isn't your lucky day"

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