arXiv:2602.08335v2 Announce Type: replace Abstract: Integrating Large Language Models (LLMs) with external tools via multi-agent systems offers a promising new paradigm for decomposing and solving complex problems. However, training these systems remains notoriously difficult due to the credit assignment challenge, as it is often unclear which specific functional agent is responsible for the success or failure of decision trajectories. Existing methods typically rely on sparse or globally broadcast rewards, failing to capture individual contributions and leading to inefficient reinforcement le
The rapid advancement and integration of Large Language Models into multi-agent systems necessitate robust methods for credit assignment to enable effective training and deployment.
A strategic reader should care because resolving the credit assignment problem is a critical bottleneck for developing truly autonomous and complex AI agent systems, impacting their reliability and effectiveness in real-world applications.
The proposed SHARP method introduces a novel Shapley credit-based optimization, potentially offering a more efficient and accurate way to train multi-agent LLM systems by precisely attributing success or failure.
- · AI agent developers
- · Organizations deploying LLM-based multi-agent systems
- · AI research institutions specializing in multi-agent reinforcement learning
- · Developers relying on sparse reward systems
- · Companies with inefficient multi-agent training pipelines
Improved performance and reliability of complex AI agent systems.
Accelerated development and adoption of AI agents across various industries, collapsing certain white-collar workflows.
Increased competition among AI agent platforms, leading to more sophisticated and specialized agentic solutions.
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Read at arXiv cs.AI