Collective communication (CC) is critical for scaling distributed machine learning (DML). The predictable traffic patterns of DML present a great opportunity for applying optical network technologies. Optical networks with reconfigurable topologies promise high bandwidth and low latency for collective communications. However, existing approaches face inherent limitations: static topologies are inefficient for dynamic communication patterns within CC algorithm, while frequent topology reconfiguration matching every step of the algorithm incurs significant overhead. In this paper, we propose SWOT, a demand-aware optical network framework that employs ``intra-collective reconfiguration'' to dynamically align network resources with CC traffic patterns. SWOT hides reconfiguration latency by overlapping it with data transmission through three key techniques: \textit{Heterogeneous Message Splitting}, \textit{Asynchronous Overlapping}, and \textit{Topology Bypassing}. Extensive simulations demonstrate that SWOT reduces communication completion time up to 89.7% across diverse CC algorithm compared to static baselines, demonstrating strong robustness to varying optical resources and reconfiguration delay.

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