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RUMAD: Reinforcement-Unifying Multi-Agent Debate

arXiv:2602.23864v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus formation, and computational efficiency. Static topology methods lack adaptability to task complexity variations, while external LLM-based coordination risks introducing privileged knowledge that compromises debate neutrality. This work presents RUMAD (Reinforcement-Unifying Multi-Agent Debate), a novel framework that formulates dynamic communication topology control in MAD as a reinforcement learning (RL) problem. RUMAD employs a content-agnostic observation scheme that captures high-level debate dynamics avoiding access to raw agent reasoning content. RUMAD uses a multi-objective reward to model solution quality, cohesion and efficiency. A PPO-trained controller dynamically adjusts edge weights in the communication graph, while a dual-threshold mechanism e

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Chao Wang, Han Lin, Huaze Tang, Huijing Lin, Wenbo Ding · · 1 min read · 2 views
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arXiv:2602.23864v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems leverage collective intelligence to enhance reasoning capabilities, yet existing approaches struggle to simultaneously optimize accuracy, consensus formation, and computational efficiency. Static topology methods lack adaptability to task complexity variations, while external LLM-based coordination risks introducing privileged knowledge that compromises debate neutrality. This work presents RUMAD (Reinforcement-Unifying Multi-Agent Debate), a novel framework that formulates dynamic communication topology control in MAD as a reinforcement learning (RL) problem. RUMAD employs a content-agnostic observation scheme that captures high-level debate dynamics avoiding access to raw agent reasoning content. RUMAD uses a multi-objective reward to model solution quality, cohesion and efficiency. A PPO-trained controller dynamically adjusts edge weights in the communication graph, while a dual-threshold mechanism enables fine-grained control over both agent activation and information visibility. Experimental evaluation across MMLU, GSM8K, and GPQA benchmarks demonstrates that RUMAD achieves substantial efficiency gains, reducing token costs by over 80\%, while still improving reasoning accuracy compared to single LLM model and multiple MAD baselines. Notably, RUMAD trained exclusively on MMLU exhibits robust zero-shot generalization to out-of-domain (OOD) tasks, indicating that the learned communication strategies capture task-independent principles of effective multi-agent coordination. These results establish RUMAD as a efficient and robust approach for deploying multi-agent reasoning application with practical resource constraints.

Executive Summary

This article introduces RUMAD, a novel framework for multi-agent debate (MAD) systems that leverages reinforcement learning to optimize accuracy, consensus formation, and computational efficiency. RUMAD's dynamic communication topology control enables efficient information exchange and reduces token costs by over 80%. The framework's robustness is demonstrated through zero-shot generalization to out-of-domain tasks. While RUMAD achieves impressive results, its performance on specific benchmarks may be limited by the quality of the underlying LLM models. The framework's potential for real-world applications, such as AI-assisted decision-making, is significant. Further evaluation of RUMAD's robustness to noisy or adversarial inputs is necessary to fully assess its practical viability.

Key Points

  • ▸ RUMAD employs a reinforcement learning-based approach to optimize MAD system performance
  • ▸ The framework's dynamic communication topology control reduces token costs by over 80%
  • ▸ RUMAD achieves zero-shot generalization to out-of-domain tasks

Merits

Efficient Information Exchange

RUMAD's dynamic communication topology control enables efficient information exchange between agents, reducing computational costs and improving system performance.

Robust Zero-Shot Generalization

RUMAD's ability to generalize to out-of-domain tasks demonstrates its robustness and potential for real-world applications.

Improved Accuracy

RUMAD's optimization framework improves reasoning accuracy compared to single LLM models and multiple MAD baselines.

Demerits

Dependence on LLM Quality

RUMAD's performance on specific benchmarks may be limited by the quality of the underlying LLM models.

Limited Evaluation of Robustness

Further evaluation of RUMAD's robustness to noisy or adversarial inputs is necessary to fully assess its practical viability.

Expert Commentary

RUMAD represents a significant advancement in the field of multi-agent debate systems, leveraging reinforcement learning to optimize performance and achieve impressive results. However, further evaluation of the framework's robustness and limitations is necessary to fully assess its practical viability. The development of RUMAD and similar frameworks also raises important questions about the role of AI in decision-making processes and the need for transparency and accountability in AI systems.

Recommendations

  • ✓ Further evaluation of RUMAD's robustness to noisy or adversarial inputs is necessary to fully assess its practical viability.
  • ✓ Investigation of RUMAD's performance on more complex and nuanced benchmarks is necessary to fully understand its limitations and potential applications.

Sources

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