Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning

Designing effective auxiliary rewards for cooperative multi-agent systems remains challenging, as misaligned incentives can induce suboptimal coordination, particularly when sparse task rewards provide insufficient grounding for coordinated behavior. This study introduces an autonomous reward design framework that uses large language models (LLMs) to synthesize executable reward programs from environment instrumentation. The procedure constrains candidate programs within a formal validity envelo

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ethics.ai (25 March 2026), “Large Language Model Guided Incentive Aware Reward Design for Cooperative Multi-Agent Reinforcement Learning,” evidence record 6766, https://ethics.ai/record/6766 (originally published by arXiv).

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