A Large Recurrent Action Model: xLSTM enables Fast Inference for
  Robotics Tasks
Authors: name: Thomas Schmied, name: Thomas Adler, name: Vihang Patil, name: Maximilian Beck, name: Korbinian Pöppel, name: Johannes Brandstetter, name: Günter Klambauer, name: Razvan Pascanu, name: Sepp Hochreiter

In recent years, there has been a trend in the field of Reinforcement
Learning (RL) towards large action models trained offline on large-scale
datasets via sequence modeling. Existing models are primarily based on the
Transformer architecture, which result in powerful agents. However, due to slow
inference times, Transformer-based approaches are impractical for real-time
applications, such as robotics. Recently, modern recurrent architectures, such
as xLSTM and Mamba, have been proposed that exhibit parallelization benefits
during training similar to the Transformer architecture while offering fast
inference. In this work, we study the aptitude of these modern recurrent
architectures for large action models. Consequently, we propose a Large
Recurrent Action Model (LRAM) with an xLSTM at its core that comes with
linear-time inference complexity and natural sequence length extrapolation
abilities. Experiments on 432 tasks from 6 domains show that LRAM compares
favorably to Transformers in terms of performance and speed.