OOM-RL: Market-Driven Alignment Replaces Human/AI Feedback for Multi-Agent Systems

arXiv·medium signal

Liu and Chen introduce Out-of-Money Reinforcement Learning, an alignment paradigm for multi-agent software engineering systems that replaces RLHF/RLAIF with market mechanisms. The paper argues that human feedback induces sycophancy while execution-based environments suffer from test evasion by unconstrained agents. OOM-RL deploys agents with budget constraints, using market signals as the objective alignment function — a novel approach to the alignment tax problem in production MAS.