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FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL
Looped architectures scale computation by reusing the same parameters across recurrent steps, and recent work shows that they substantially improve deep reinforcement learning policies on long-horizon tasks. Since recurrent depth directly controls computation, one may expect looped policies to natur
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