Although I don't disagree with your general assessment, I do think it's a bit misplaced.
The model's parameters are larger than the weights of the neural network used for in the model. Hell, the model could be written as just a huge Python file full of if-else statements, and that code would still be correctly identified as the model's parameters. So it's a bit obtuse/arbitrary to decide that the term "recursive self-improvement" should be limited to just updating the model's neural net's weights.
Doesn't mean there shouldn't be more precise terminology, mind you. But I believe the way everyone has been using the term "recursive self-improvement" is perfectly accurate given how general that term is.
The model's parameters are larger than the weights of the neural network used for in the model. Hell, the model could be written as just a huge Python file full of if-else statements, and that code would still be correctly identified as the model's parameters. So it's a bit obtuse/arbitrary to decide that the term "recursive self-improvement" should be limited to just updating the model's neural net's weights.
Doesn't mean there shouldn't be more precise terminology, mind you. But I believe the way everyone has been using the term "recursive self-improvement" is perfectly accurate given how general that term is.