GARDEN: LLM-driven generalist reward design for cooperative multi-agent RL
The paper introduces GARDEN (Generalist Autonomous Reward Design through Evidence Navigation), an approach using large language models as autonomous reward engineers to produce a single reward program that transfers unchanged across related cooperative multi-agent reinforcement learning scenarios. On unseen Overcooked and SMAX scenarios, GARDEN increased task returns by 58.2% and 52.5% over default rewards and raised mean win rate in two combat scenarios from 51.0% to 89.9%, outperforming fixed diagnostic feedback and Eureka-inspired reward reflection under matched budgets.