None of the labs would be doing undirected drift. That wouldn’t yield improvement for exactly the reasons you suggest.
In the absence of a ground truth quality/correctness signal, optimizing for coherence works. This can give prettier answers (in the way that averaged faces are prettier) but this is limited. The inference time scaling equivalent would be a branching sampling approach that searches for especially preferred token sequences rather than the current greedy sampling approach. Optimising for idea level coherence can improve model thinking to some extent.
For improving raw intelligence significantly, ground truth is necessary. That’s available in STEM domains, computer programming tasks being the most accessible. One can imagine grounding hard engineering the same way with a good mechanical/electrical simulation package. TLDR:train for test-time performance.
Then just cross your fingers and hope for transfer learning into softer domains.
For softer domains, ground truth is still accessible via tests on humans (EG:optimise for user approval). This will eventually yield super-persuaders that get thumbs up from users. Persuasion performance is trainable but maybe not a wise thing to train for.
As to actually improving some soft domain skill like “write better english prose” that’s not easy to optimise directly as you’ve observed.
None of the labs would be doing undirected drift. That wouldn’t yield improvement for exactly the reasons you suggest.
In the absence of a ground truth quality/correctness signal, optimizing for coherence works. This can give prettier answers (in the way that averaged faces are prettier) but this is limited. The inference time scaling equivalent would be a branching sampling approach that searches for especially preferred token sequences rather than the current greedy sampling approach. Optimising for idea level coherence can improve model thinking to some extent.
For improving raw intelligence significantly, ground truth is necessary. That’s available in STEM domains, computer programming tasks being the most accessible. One can imagine grounding hard engineering the same way with a good mechanical/electrical simulation package. TLDR:train for test-time performance.
Then just cross your fingers and hope for transfer learning into softer domains.
For softer domains, ground truth is still accessible via tests on humans (EG:optimise for user approval). This will eventually yield super-persuaders that get thumbs up from users. Persuasion performance is trainable but maybe not a wise thing to train for.
As to actually improving some soft domain skill like “write better english prose” that’s not easy to optimise directly as you’ve observed.