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on a call, i was discussing my idea for doing activation-level learning to (hopefully) provide models feedback based on their internal computations and choices:
I may have slipped into a word game... are we "training against the [interpretability] detection method" or are we "providing feedback away from one kind of algorithm and towards another"? They seem to suggest very different generalizations, even though they describe the same finetuning process. How could that be?