title: A Large Recurrent Action Model: xLSTM enables Fast Inference for
    Robotics Tasks
summary: 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.
paper_authors._id: 67233b27548a7844da459d6d
paper_authors.name: Thomas Schmied
paper_authors._id: 67233b27548a7844da459d6e
paper_authors.name: Thomas Adler
paper_authors._id: 67233b27548a7844da459d6f
paper_authors.name: Vihang Patil
paper_authors._id: 67233b27548a7844da459d70
paper_authors.name: Maximilian Beck
paper_authors._id: 67233b27548a7844da459d71
paper_authors.name: Korbinian Pöppel
paper_authors._id: 67233b27548a7844da459d72
paper_authors.name: Johannes Brandstetter
paper_authors._id: 67233b27548a7844da459d73
paper_authors.name: Günter Klambauer
paper_authors._id: 67233b27548a7844da459d74
paper_authors.name: Razvan Pascanu
paper_authors._id: 67233b27548a7844da459d75
paper_authors.name: Sepp Hochreiter
paper_title: A Large Recurrent Action Model: xLSTM enables Fast Inference for
    Robotics Tasks
paper_summary: 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.
paper_ai_summary: A Large Recurrent Action Model (LRAM) using xLSTM architecture achieves comparable performance and faster inference speed than Transformer-based models for large action models.
paper_ai_keywords: xLSTM Mamba recurrent architectures Transformer architecture large action models sequence length extrapolation inference complexity