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cs.LG2026
Length-Independent State Tracking Under a Parallel Scan
Julien Brandoit, Arthur Fyon, Thomas Braipson +5
Learning robust and scalable finite-state tracking is fundamental to sequence processing. While linear recurrent neural networks (RNNs), linear attention, and state space models en…
cs.LG2026
Train Short, Act Long: Multistability and the Horizon Independence of Recurrent Policies
Asad Bakija, Florent De Geeter, Julien Brandoit +2
In partially observable reinforcement learning (RL), a decision often depends on a cue seen many steps earlier, and training at such horizons is costly. Ideally, a policy trained o…