FlexLoop: Depth-Elastic Looped Policies for Adaptive Test-Time Computation in Deep RL
Post-trains a looped reinforcement-learning policy to make useful decisions at several recurrent depths. Combines its original objective with distillation between neighboring depths, then uses agreement across depths to decide when to stop.
First public: · Added to this list: October 18, 2026
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arXiv:2609.34488 · Initial submission: 2026-09-28
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Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
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