用xLSTM构建大模型，让机器人推理更快更准。
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics Tasks
- 以xLSTM为核心构建大尺度动作模型，支持线性推理速度
- 6个领域432项任务中表现优于或媲美Transformer，且推理更快
- 适合对实时性要求高的机器人控制场景
近年来，强化学习领域兴起基于大规模数据集离线训练的大型动作模型，多采用Transformer架构。但其推理速度慢，难以用于机器人等实时应用。近期提出的xLSTM等新型循环架构，在训练时具备与Transformer相当的并行化能力，同时实现快速推理。本文研究此类架构在大型动作模型中的适用性，提出基于xLSTM的核心结构——大型循环动作模型（LRAM），具有线性时间推理复杂度和自然序列长度外推能力。在6个领域共432项任务上的实验表明，LRAM在性能和速度上均优于或媲美Transformer，尤其适合需要快速响应的机器人任务。
原文摘要 · Abstract (English)
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.
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