Reinforcement learning (RL) under partial observability typically relies on recurrent networks to encode temporal information. The recurrent module is usually trained jointly with the policy or value function, a standard design choice in the field.
A new arXiv paper (2610.13197) poses a natural question: does learning the recurrent network matter? The abstract, as provided, introduces this question but is truncated before presenting any findings or conclusions. The paper appears to examine whether this joint training is necessary, but the available text does not reveal the outcome.
The question is significant because it challenges a widely used assumption. If recurrent learning is not essential, simpler or fixed encoders could potentially replace trained recurrent modules. However, the source does not provide evidence to support either answer.