Vision-language-action (VLA) models have shown strong potential for general-purpose robotic manipulation, but extending them to bimanual dexterous manipulation introduces a high-dimensional and structured action space. Existing bimanual VLA policies commonly concatenate right- and left-side arm--hand actions at each control timestep, leaving cross-hand interactions implicit within a single action vector. We introduce Coordi-$π$, a structured action representation for bimanual dexterous manipulation. Coordi-$π$ represents the right- and left-side arm--hand actions as individual action tokens and organizes them into an interleaved sequence, explicitly exposing the two sides within the action sequence. To explicitly encode hand identity and temporal synchronization in this interleaved representation, a learnable hand-identity embedding distinguishes the two sides, while sequence--control-timestep factorized rotary positional encoding (RoPE) separates sequence position from control timestep, allowing actions at the same control timestep to share a temporal coordinate while retaining distinct sequence positions. Across all five bimanual dexterous manipulation tasks in DexJoCo, Coordi-$π$ achieves state-of-the-art performance under both \textit{rand-obj} and \textit{rand-full}, improving the average success rate over $π_{0.5}$ from 31.9\% to 51.3\% and from 18.3\% to 32.3\%, respectively. Coordi-$π$ also consistently outperforms $π_{0.5}$ across four real-world bimanual dexterous manipulation tasks.

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