SHARP framework optimizes multi-agent LLM training with Shapley credit

Researchers have developed a new framework called SHARP to improve the training of multi-agent systems that integrate large language models with external tools. This method addresses the challenge of assigning credit to individual agents for successful outcomes, which is crucial for efficient learning. SHARP utilizes a decomposed reward mechanism, including a Shapley-based marginal-credit reward, to precisely attribute contributions and stabilize training. Experiments show SHARP significantly outperforms existing methods, achieving substantial improvements in accuracy and efficiency. AI

IMPACT Enhances training efficiency for complex multi-agent LLM systems, potentially accelerating their adoption in real-world problem-solving.

RANK_REASON The cluster contains an academic paper detailing a new framework for multi-agent systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Large Language Models
• SHARP
• Xuelin Zhang

• paper
• model release

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