Gradient modifications before Adam inflate old-direction learning rates via the second-moment term, but routing modifications solely to the first moment with adaptive strength prevents collapse and yields 3.8-4.8 unit gains over baselines in 8- and 16-domain continual learning.
0 500 1000 1500 2000 Training step 0.2 0.4 0.6 0.8 1.0 v(u) t /σ2 σ2 window mean = 0.26 pred
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Hidden Failure Modes of Gradient Modification under Adam in Continual Learning, and Adaptive Decoupled Moment Routing as a Repair
Gradient modifications before Adam inflate old-direction learning rates via the second-moment term, but routing modifications solely to the first moment with adaptive strength prevents collapse and yields 3.8-4.8 unit gains over baselines in 8- and 16-domain continual learning.