Before revision:  In spite of this, and unlike previous first-order methods, our rule recovers an arbitrarily accurate meta-parameter update given enough compute . As such, contrastive meta-learning is a candidate rule for biologically-plausible meta-learning .
After revision:  In spite of this, and unlike previous first-order methods, our rule recovers an arbitrarily accurate meta-parameter update given enough compute .