Overlapping Subspace Communication for Spiking Decision Transformer

Offline reinforcement learning can be formulated as sequence modeling, enabling policies to learn directly from fixed trajectories. While spiking Decision Transformers provide an efficient alternative to dense Transformers through sparse event-driven computation, the internal communication structure of sparse spiking attention remains underexplored. We observe that spike-driven communication supports vary across return-ranked trajectories, while a subset of token interactions repeatedly recurs, suggesting the coexistence of reusable and context-dependent communication patterns. Motivated by this observation, we propose the Spiking Subspace Decision Transformer (SS-DT) with Overlapping Spiking Subspace Attention (OSSA). OSSA factorizes intra-head spike communication into multiple overlapping subspaces, where shared components capture reusable structures and private components preserve context-specific variations. Spike-guided allocation dynamically composes these subspaces according to spiking activity, while predictive feedback and return-conditioned modulation further adapt communication to trajectory dynamics and decision context. Together, SS-DT introduces structured adaptive communication into sparse spiking sequence modeling. Across 21 tasks on MuJoCo and Adroit, SS-DT outperforms the strongest spiking baseline by 3.7 normalized-return points on average, with gains of 1.3 and 5.4 points on MuJoCo and Adroit, respectively, establishing state-of-the-art average performance among spiking offline RL methods.