Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

• URL: http://arxiv.org/abs/2504.14520v1
• Date: Sun, 20 Apr 2025 07:34:26 GMT
• Title: Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey
• Authors: Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Muhammad Awais Khan Bangash, Muhammad Ali Jamshed,
• Abstract summary: This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective.<n>By exploring reward mechanisms, self-play, and continuous learning methods in MARL, this survey gives a comprehensive roadmap to building introspective, adaptive, and trustworthy LLMs.
• Score: 2.572335031488049
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective. Meta-thinking self-reflection, assessment, and control of thinking processes is an important next step in enhancing LLM reliability, flexibility, and performance, particularly for complex or high-stakes tasks. The survey begins by analyzing current LLM limitations, such as hallucinations and the lack of internal self-assessment mechanisms. It then talks about newer methods, including RL from human feedback (RLHF), self-distillation, and chain-of-thought prompting, and each of their limitations. The crux of the survey is to talk about how multi-agent architectures, namely supervisor-agent hierarchies, agent debates, and theory of mind frameworks, can emulate human-like introspective behavior and enhance LLM robustness. By exploring reward mechanisms, self-play, and continuous learning methods in MARL, this survey gives a comprehensive roadmap to building introspective, adaptive, and trustworthy LLMs. Evaluation metrics, datasets, and future research avenues, including neuroscience-inspired architectures and hybrid symbolic reasoning, are also discussed.

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