SSL models trained on real observations can be thought of as maps, so that tuning them without keeping this point of view in mind risks changing the map in a way that is motivated by something other than aligning it with the territory.
In particular, fine-tuning might distort the map, and amplification might generate sloppy fiction to be accepted as territory. A proper use of fine-tuning in this frame is as search for high fidelity depictions of aligned agents, zooming in the map by conditioning it to be about particular situations we are looking for. This is different from realigning the whole map with reinforcement learning, which might get it to lose touch with the ground truth of original training data. And a proper use of amplification is as reflection on blank spaces on the map, or low fidelity regions, extrapolating past/​future details and other hidden variables of the territory from what the map does show, and learning how it looks when added to the map.
SSL models trained on real observations can be thought of as maps, so that tuning them without keeping this point of view in mind risks changing the map in a way that is motivated by something other than aligning it with the territory.
In particular, fine-tuning might distort the map, and amplification might generate sloppy fiction to be accepted as territory. A proper use of fine-tuning in this frame is as search for high fidelity depictions of aligned agents, zooming in the map by conditioning it to be about particular situations we are looking for. This is different from realigning the whole map with reinforcement learning, which might get it to lose touch with the ground truth of original training data. And a proper use of amplification is as reflection on blank spaces on the map, or low fidelity regions, extrapolating past/​future details and other hidden variables of the territory from what the map does show, and learning how it looks when added to the map.