Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning
Uses a shared transformer block to refine a reinforcement-learning policy's internal state before choosing an action. Learns to vary the number of loops with the current state, and studies adaptive computation in puzzles and robotic-control tasks.
First public: · Added to this list: October 18, 2026
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arXiv:2609.37432 · Initial submission: 2026-09-29
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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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