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I'm wondering if you could fine tune the model on an aggregate of a temporal slice of revalued weights? Something analogous to REM sleep's involvement in embedding the days events into long term memory.


Sieve the temporary backprop interim weights as a function of its loss of varentrophy relative to its place in the revalued weights.

Remove the bottom weights dynamically based on the local gradient in varentrophy so that internal dissonance ("doubt") can be selected against.

"Preference Optimization" but with more opportunities for meta-optimization.




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