I believe he’s talking about some sort of ‘energy as measured by distance from the models understanding of the world’ as in quite literally a world model. But again I’m ignorant, hence the post!
In some respects that sounds similar to what we already do with reward models. I think with GRPO, the “bag of rewards” approach doesn’t strike me as terribly different. The challenge is in building out a sufficient “world” of rewards to adequately represent more meaningful feedback-based learning.
While it sounds nice to reframe it like a physics problem, it seems like a fundamentally flawed idea, akin to saying “there is a closed form solution to the question of how should I live.” The problem isn’t hallucinations, the problem is that language and relativism are inextricably linked.
When an architecture is based around world model building, then it is a casual outcome that similar concepts and things end up being stored in similar places. They overlap.
As soon as your solution starts to get mathematically complex, you are departing from what the human brain does. Not saying that in some universe it might be possible to make a statistical intelligence, but when you go that direction you are straying away from the only existing intelligences that we know about. The human brain. So the best solutions will closely echo neuroscience.
This sort of measure is a decent match for BPB though. BPB=-log(document_probability)/document_length_bytes and perplexity=e^(BPB*document_length_bytes/document_length_tokens). We already train models by minimizing perplexity, and model outputs are already those that are high probability. Though like with EBMs, figuring out outputs with even higher probability would require an expensive search step.
