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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

Japanese

ディスプレッシャーはリスクを増強するが、ディスプレッシャーはリスクを増強するための特定の利益政策であるため、明らかな6つの建築的利益は、アーキテクチャの再生可能な特性よりも優れたトレーニングの実現を反映する可能性があります。我々は、バニラのDouble Deep Q-Learning(DDQL)のK-means-partitioned mixtures of DDQL experts at $K \in \{2, 4, 8\}$、および密集したネットワークパラメータは、アーキテクチャの再生可能な特性に比べて有利な実現を反映します。我々は、ビナンスからの5分のKのDouble Deep Q-Learning(DDQL)、K-means-partitioned KTC/USD

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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