Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910915230695424 |
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| author | Bilal, Ahsan Mohsin, Muhammad Ahmed Umer, Muhammad Bangash, Muhammad Awais Khan Jamshed, Muhammad Ali |
| author_facet | Bilal, Ahsan Mohsin, Muhammad Ahmed Umer, Muhammad Bangash, Muhammad Awais Khan Jamshed, Muhammad Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14520 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Bilal, Ahsan Mohsin, Muhammad Ahmed Umer, Muhammad Bangash, Muhammad Awais Khan Jamshed, Muhammad Ali Artificial Intelligence Computation and Language 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. |
| title | Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2504.14520 |