Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state into a continuous control signal, has barely changed and remains a single-point predictor across the majority of current VLAs. Whether implemented via autoregressive token bins, L1 regression, or flow-matching denoising, the resulting decoder treats the action space as unstructured, leaving the geometric proximity of neighboring actions unexploited during training. To advance this, we introduce ActionMap, a voxel heatmap action head that drops into an existing VLA in place of its native action decoder. For each new action, the head predicts a voxel heatmap over the action space, where each voxel directly stores the probability of the corresponding action. Across LIBERO simulation and real-world Franka manipulation, our heatmap head surpasses two architecturally distinct backbones at matched training steps (e.g., +8.2% over OpenVLA-OFT's L1 regression head on the LIBERO four-suite average), converges at comparable or faster rates on both backbones, and remains markedly more data-efficient at low training data. The cross-backbone consistency indicates that action representation is a real lever for VLA performance, distinct from further backbone or recipe scaling. Project Page: https://showlab.github.io/ActionMap/.

翻译：视觉-语言-动作（VLA）模型在主干网络、训练策略和数据规模方面取得了快速进展，但将主干网络隐藏状态转换为连续控制信号的动作解码器在大多数现有VLA中几乎未发生变化，仍为单点预测器。无论通过自回归token分箱、L1回归还是流匹配去噪实现，此类解码器均将动作空间视为非结构化，导致训练过程中未能利用相邻动作的几何邻近性。为此，我们提出ActionMap——一种可替代现有VLA原生动作解码器的体素热图动作头。对于每个新动作，该动作头在动作空间上预测一个体素热图，其中每个体素直接存储对应动作的概率。在LIBERO仿真和真实Franka操控实验中，我们的热图头在相同训练步数下超越两种架构不同的主干网络（例如，在LIBERO四任务套件平均性能上较OpenVLA-OFT的L1回归头提升8.2%），在两种主干网络上实现相当或更快的收敛速度，且在低训练数据量下保持显著更高的数据效率。跨主干网络的一致性表明，动作表示是区别于主干网络或训练策略扩展的VLA性能实际杠杆。项目页面：https://showlab.github.io/ActionMap/。