Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning
arXiv:2607.04470v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement learning (MARL), yet the training-time dynamics of this integration remain poorly understood. We show that dynamically updating LLM-generated reward weights during off-policy MARL violates the stationarity assumption of Potential-Based Reward Shaping (PBRS) and contaminates the experience replay buffer, whose stored transitions carry reward labels computed under stale shaping weights. We character
The rapid advancement and integration of LLMs into various AI paradigms, including multi-agent reinforcement learning, necessitate fundamental research into their actual operational dynamics and stability.
This research highlights critical challenges in building robust and reliable LLM-augmented AI systems, directly impacting the safety, effectiveness, and scalability of advanced AI applications.
Understanding these integration pitfalls will lead to more principled and stable methods for combining LLMs with MARL, informing future research and development in agentic systems.
- · AI researchers focusing on foundation models and MARL
- · Developers of stable AI agent frameworks
- · Applications requiring high-assurance AI cooperation
- · Overly simplistic LLM-MARL integration strategies
- · Systems relying on naive reward shaping with LLMs
- · Applications demanding real-time dynamic LLM-rewards without safeguards
The paper identifies specific technical challenges in integrating Large Language Models (LLMs) with multi-agent reinforcement learning (MARL).
This understanding will drive the development of new algorithms and theoretical frameworks for stable and reliable LLM-augmented cooperative AI agents.
Improved stability in multi-agent LLM systems will accelerate the deployment of autonomous AI agents in complex, real-world cooperative tasks, potentially impacting various industries.
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Read at arXiv cs.AI