jelius

LLM based multi-agent systems (MAS) for collaborative problem-solving. **Original Problem** 🔍: LLM-based multi-agent systems (MAS) face challenges in communication efficiency, scalability, and optimization methods. ----- **Solution in this Paper** 🧠: OPTIMA framework: • Iterative generate, rank, select, and train paradigm • Reward function balancing task performance, token efficiency, and readability • Explores SFT, DPO, and hybrid approaches • Integrates MCTS-inspired techniques for DPO data generation ----- **Key Insights from this Paper** 💡: • OPTIMA significantly improves both communication efficiency and task effectiveness • Demonstrates potential for enhancing inference-time scaling laws • Shows ability to transfer knowledge to out-of-distribution tasks • Highlights importance of efficient communication in MAS and LLM systems ----- **Results** 📊: • Up to 2.8x performance gain with <10% tokens on information exchange tasks • Consistent outperformance over single-agent baselines and vanilla MAS • Improved inference-time scaling laws • Effective knowledge transfer to out-of-distribution tasks

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