When Does Backpropagating Through Policy Memory Matter? Physical Credit, Optimizer Updates, and Observability
Policies with memory can learn along two backward paths: through the physical states their actions produce and through the representations they store. Transformer-XL and truncated backpropagation through time cut the second path at stored history while keeping its values. We ask when this cut matters. Holding the forward computation fixed and varying only derivative edges, we measure parameter gradients, the updates the optimizer applies, and continued training in a Transformer vessel-trajectory model and a quadrotor tracking policy. In the vessel model, detaching the key-value cache shrank the gradient to about a tenth of its norm, with little rotation, when gradients flowed through all ...
| Authors | Xingjian Li, Jianhua Z. Huang, Junli Duan |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 3 October 2026 |
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