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arXiv Machine Learning By Alexander Ardaiz, Varun Budati, Ali Habibnia

Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes

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The study investigates whether mixture‑of‑experts (MoE) architectures improve deep reinforcement‑learning policies for cryptocurrency order execution. Using 5‑minute BTC/USDT limit‑order‑book data, the authors compare vanilla Double Deep Q‑Learning (DDQL) with K‑means‑partitioned MoE experts (K=2,4,8) and dense networks matched in size to the expert budgets. Across 100 training seeds, none of the MoE variants reduce mean implementation shortfall relative to DDQL, and all perform worse than simple benchmarks (TWAP and immediate liquidation). The authors find that training‑specification issues—particularly exploration schedules—drive most failures, while MoE partitioning offers no intrinsic benefit and can mask underlying problems. They also report that higher K increases tail risk and that performance variability depends heavily on seed selection.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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