arXiv Machine Learning July 14, 2026 By Wen-Ting Wang

Reinforcement Learning for Execution under Dynamic Fees in a Closed-Loop DEX Simulator

Read the original on arXiv Machine Learning →

arXiv:2607. 10960v1 Announce Type: new Abstract: Trader-facing dynamic fees are increasingly proposed for automated market makers (AMMs), but historical data do not identify how order flow would respond: trader-facing fees do not vary, trader types are latent, and a replayed tape is not a sequential decision environment.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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