I’d go so far as to add one more layer to monitor this one and stop adding layers. My thinking is that this meta awareness is all you need.
No data to back my hypothesis up. So take it for what it’s worth.
this is just standard decoding, the stream of vectors is called the k/v cache
It's also so not far from Meta's large concept model idea.
[41 comments, 166 points] https://news.ycombinator.com/item?id=42919597
For example, xenophobia as a response to economic hardship is the wrong chain of thinking embedded in the larger zeitgeist.
I reflected on the pop-psychology idea of consciousness and subconsciousness. I thought of each as an independent stream of tokens, like stream of consciousness poetry. But along the stream there were joining points between these two streams, points where the conscious stream was edited by the subconscious stream. You could think of the subconscious stream as performing CRUD like operations on the conscious stream. The conscious stream would act like a buffer of short-term memory while the subconscious stream would act like a buffer of long-term memory. Like, the subconscious has instructions related to long-term goals and the conscious stream has instructions related to short-term goals.
You can imagine perception as input being fed into the conscious stream and then edited by the subconscious stream before execution.
It seems entirely possible to actually implement this idea in this current day and age. I mean, it was a fever dream as a kid, but now it could be an experiment!
And it would have to be RL for your idea to work since there is no "thinking" dataset for a novel token space. There isn't even one for existing LLM token space, but they have the base model to work off of. When the thought is expressed in English, the model already knows the relationships between the tokens in the thought, it's merely repurposing it for a "thinking" application.
Wait What? That is an odd way of defining it. That's like saying turing machines are inefficient way to solve TSP. You would , at the least, want to define this in terms of complexity or put this into context of domains and observability.
RL's by definition is a field that is about finding efficient problems in the domain of choice[1]. There are likely regimes in LLM/LRM learning where RL can be quite efficient, polynomial time even in the state space, we just need to explore and find them. For example you can use Dynamic Programming as a "more" efficient way to solve MDPs[1] because it is polynomial in the state space X Action space.
[1]https://web.stanford.edu/class/psych209/Readings/SuttonBarto...
What the OP suggested is similar to training a transformer from scratch using RL (ie. no training tokens) towards an objective of steering a pretrained LLM to produce human readable output. It will probably not even converge, and if it does it would take immense compute.
The downside is that you are limiting the model to think in the same language it outputs. An argument could be made that this is not how all humans think. I know that I rarely think in language or even images, just concepts (probably isn't even the right word) mix and transform and often I don't even bother to make the transformation to language at the end, just action.
At the time I had this idea I did not know of either of these. I think I was drawing explicitly on the conscious / subconscious vocabulary.