Cheap Retrieval and Observation Credit Assignment from One Quantity: The Value of Information
arXiv:2610.40030v1 Announce Type: new Abstract: Memory in reinforcement learning raises two problems. Attention finds the relevant item by scoring the whole store, which is costly for robots under latency and energy limits. When a retrieved memory pays off, credit for acquiring it must cross the whole sequence. We argue that both are governed by one quantity, the value of information, and propose to estimate it from archived rollouts with a learned world model. We obtain that, when read at the…