🤖 AI Summary
This work addresses key challenges in multi-agent reinforcement learning—such as ambiguous credit assignment, environmental non-stationarity, and complex agent interactions—stemming from handcrafted reward functions. To overcome these limitations, the paper proposes leveraging large language models (LLMs) to directly translate natural language objectives into semantic reward signals, replacing conventional hand-designed numerical rewards. The approach restructures coordination mechanisms through three pillars: semantic reward specification, dynamic adaptation, and alignment with human intent, enabling agents to collaborate based on shared semantic representations rather than explicit numeric cues. By integrating LLMs (e.g., EUREKA, CARD) with the Verifiable Reward Reinforcement Learning (RLVR) framework, the method achieves language-driven reward generation and online optimization. Experimental results demonstrate that this paradigm significantly reduces manual intervention while substantially improving alignment between multi-agent behavior and human intentions.
📝 Abstract
Reward engineering, the manual specification of reward functions to induce desired agent behavior, remains a fundamental challenge in multi-agent reinforcement learning. This difficulty is amplified by credit assignment ambiguity, environmental non-stationarity, and the combinatorial growth of interaction complexity. We argue that recent advances in large language models (LLMs) point toward a shift from hand-crafted numerical rewards to language-based objective specifications. Prior work has shown that LLMs can synthesize reward functions directly from natural language descriptions (e.g., EUREKA) and adapt reward formulations online with minimal human intervention (e.g., CARD). In parallel, the emerging paradigm of Reinforcement Learning from Verifiable Rewards (RLVR) provides empirical evidence that language-mediated supervision can serve as a viable alternative to traditional reward engineering. We conceptualize this transition along three dimensions: semantic reward specification, dynamic reward adaptation, and improved alignment with human intent, while noting open challenges related to computational overhead, robustness to hallucination, and scalability to large multi-agent systems. We conclude by outlining a research direction in which coordination arises from shared semantic representations rather than explicitly engineered numerical signals.