An external controller for frozen LLMs raises strict validation success on three RL coding tasks from 0/9 to 8/9 by selecting memory records and skills, running fail-fast checks, and propagating credit via eligibility traces.
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OOM-RL aligns multi-agent LLM systems for software engineering by using real financial market losses as an un-hackable negative gradient, resulting in a mature-phase annualized Sharpe ratio of 2.06 via a strict test-driven workflow.
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PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents
An external controller for frozen LLMs raises strict validation success on three RL coding tasks from 0/9 to 8/9 by selecting memory records and skills, running fail-fast checks, and propagating credit via eligibility traces.
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OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems
OOM-RL aligns multi-agent LLM systems for software engineering by using real financial market losses as an un-hackable negative gradient, resulting in a mature-phase annualized Sharpe ratio of 2.06 via a strict test-driven workflow.