OOM-RL: Out-of-Money Reinforcement Learning - Market-Driven Alignment for LLM-Based Multi-Agent Systems

By Kun Liu, Liqun Chen

Published 2026-04-13

Everscope rating

1145.1

Relevance to quantitative trading

9 / 10

Implementation complexity

9 / 10

Reproducibility

1 / 5

About this paper

Methodology: Out-of-Money Reinforcement Learning (OOM-RL) with Strict Test-Driven Agentic Workflow (STDAW). Problem types: Reinforcement Learning, Portfolio Optimization, Risk Management, Algorithmic Execution, Natural Language Processing, Optimization, Anomaly Detection.

arXiv:2604.11477 · Code · Paper rankings

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