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

Alexander Ardaiz, Varun Budati, Ali Habibnia · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.03369 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Las estrategias de aprendizaje específicas para la ejecución de órdenes pueden variar sustancialmente entre las semillas de entrenamiento, por lo que los ganancias arquitectónicas aparentes pueden reflejar las realizaciones de entrenamiento favorables en lugar de las propiedades reproducibles de la arquitectura. evaluamos las políticas de aprendizaje específicas de vanilla Double Deep Q-Learning (DDQL), las mezclas de K-means-partitionadas de los expertos en DDQL en $K \in \{2, 4, 8\$, y los parámetros densos de ejecución de la red favorables en lugar de las propiedades reproducibles de la arquitectura. Valoramos los presupuestos de expertos de la familia Double Deep Q-Learning (DDQL), los datos del libro de órdenes de K-means-partitionados de Binance.

Machine translation (Meta M2M100); the English original follows.

Abstract

Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations rather than reproducible properties of the architecture. We evaluate vanilla Double Deep Q-Learning (DDQL), K-means-partitioned mixtures of DDQL experts at $K \in \{2, 4, 8\}$, and dense networks parameter-matched to the $K{=}4$ and $K{=}8$ expert budgets on 5-minute mean-aggregated BTC/USDT limit order book data from Binance. No learned configuration significantly improves mean implementation shortfall over DDQL. Under the reported specification, all have higher mean shortfall than TWAP (0.39 bps) and immediate liquidation (0.21 bps) in an environment whose frictionless replay and terminal-urgency penalty make early liquidation nearly costless; 11/100 vanilla-DDQL runs, versus none in either MoE $K{\geq}4$ arm, converge to a policy that waits until forced liquidation. We then decompose this specification on a device-matched baseline. Annealed exploration alone eliminates observed collapses (12/100 to 0/100; exact McNemar $p{=}4.9{\times}10^{-4}$), matching the elimination under expert partitioning. Combining annealed exploration with the aligned reward restores collapse in 19/30 runs; with all three specification changes, it rises to 48/100. In this environment, expert partitioning is unnecessary to suppress collapse and appears to mask a training-specification failure rather than confer an intrinsic performance benefit. No MoE $K{=}8$ run collapses under any of the six specifications tested. Across-seed dispersion is lowest at $K{=}8$ but non-monotone and not robust to family-wise adjustment, while within-policy tail risk worsens monotonically with $K$. The apparent attribution of the failure mode reverses between 30 and 100 seeds, illustrating the importance of repeated-seed evaluation.

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Topics: Monotonic function, Training (meteorology), Order (exchange), Limit (mathematics), Computer science, Matching (statistics)

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