papersSEP 12 04:00 UTC
Paper frames update admission for embodied agents as error control vs. retained learning
A new arXiv paper argues that deciding whether to accept a policy update in continual embodied learning should be judged on two fronts: rejecting harmful changes and preserving useful learning. The authors propose auditing update admission at a fixed interaction budget, since overly strict validation can block beneficial adaptation. They call for evaluation that measures both error control and the learning opportunities retained.