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cs.LG2026
Looped Actor: Depth-Recurrent Reasoning Models for Reinforcement Learning
T. Konstantin Rusch, Tim Seyde, Jared Boyer +2
Looped reasoning models repeatedly apply a shared set of parameters, enabling more computation without increasing the model size. These models also support input-dependent computat…
cs.LG2026
When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift
Kevin Vogt-Lowell, Theodoros Tsiligkaridis, Rodney Lafuente-Mercado +4
Real-world reinforcement learning systems must operate under distributional drift in their observation streams, yet most policy architectures implicitly assume fully observed and n…