Beyond Orchestrating Workflows: How UnityMAS-O Trains LLM-Based Multi‑Agent Systems

UnityMAS‑O introduces a general reinforcement‑learning framework that converts predefined LLM multi‑agent workflows into trainable tasks, enabling credit assignment across roles, supporting parameter‑sharing configurations, and demonstrating significant F1 and test‑pass improvements on QA and code‑generation benchmarks.

LLMMulti-Agent Reinforcement LearningPPO

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Beyond Orchestrating Workflows: How UnityMAS-O Trains LLM-Based Multi‑Agent Systems

DataFunTalk

Feb 21, 2021 · Artificial Intelligence

Intra‑Ensemble in Neural Networks

This paper proposes an intra‑ensemble strategy that trains multiple sub‑networks within a single neural network using random training operations, width‑depth variations, and parameter sharing, achieving diverse models and improved performance comparable to traditional ensembles while adding only marginal parameter overhead.

Architecture SearchModel DiversityParameter Sharing

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