meta-gradients-defined-as-wv-xprime-value-projections

Summary

In the framework that treats in-context learning as a kind of gradient descent, the "meta-gradient" is not a free-floating concept but has a fixed mathematical identity: it is the value-projection of the few-shot examples the model sees. This matters because it means the learning signal extracted from demonstrations slots into the exact same algebraic slot as the error term in a standard gradient update, so the analogy between in-context learning and parameter optimization is structurally precise rather than merely metaphorical.