A Large Recurrent Action Model: xLSTM enables Fast Inference for
  Robotics Tasks
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.
67233b27548a7844da459d6d
Thomas Schmied
67233b27548a7844da459d6e
Thomas Adler
67233b27548a7844da459d6f
Vihang Patil
67233b27548a7844da459d70
Maximilian Beck
67233b27548a7844da459d71
Korbinian Pöppel
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2025-06-16T08:35:50.111Z
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/avatars/bfcc236641671e88c2fe5426740071d3.svg
Korbinian Poeppel
user
korbip
67233b27548a7844da459d72
Johannes Brandstetter
67233b27548a7844da459d73
Günter Klambauer
67233b27548a7844da459d74
Razvan Pascanu
67233b27548a7844da459d75
Sepp Hochreiter
A Large Recurrent Action Model: xLSTM enables Fast Inference for
  Robotics Tasks
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.
A Large Recurrent Action Model (LRAM) using xLSTM architecture achieves comparable performance and faster inference speed than Transformer-based models for large action models.
xLSTM Mamba recurrent architectures Transformer architecture large action models sequence length extrapolation inference complexity