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Agent-GSPO: Communication-Efficient Multi-Agent Systems via Group Sequence Policy Optimization

reinforcement-learning › multi-agent

📄 Abstract

Abstract: To combat the prohibitive communication costs of ``free-for-all" multi-agent systems (MAS), we introduce \textbf{Agent-GSPO}, a framework that directly optimizes for token economy using sequence-level reinforcement learning. Agent-GSPO leverages the stable and memory-efficient Group Sequence Policy Optimization (GSPO) algorithm to train agents on a communication-aware reward that explicitly penalizes verbosity. Across seven reasoning benchmarks, Agent-GSPO not only achieves new state-of-the-art performance but does so with a fraction of the token consumption of existing methods. By fostering emergent strategies like ``strategic silence," our approach provides a practical blueprint for developing scalable and economically viable multi-agent systems.

Authors (4)

Yijia Fan

Jusheng Zhang

Jing Yang

Keze Wang

Submitted

October 26, 2025

arXiv Category

cs.MA

arXiv PDF

Key Contributions

Agent-GSPO is a framework that optimizes communication efficiency in multi-agent systems by directly training for token economy using sequence-level RL and the GSPO algorithm. It achieves state-of-the-art performance on reasoning benchmarks with significantly reduced token consumption, fostering strategies like 'strategic silence'.

Business Value

Enables the development of more economically viable and scalable multi-agent systems by drastically reducing communication costs, crucial for applications like swarm robotics, distributed AI, and complex simulations.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

Moderate. Requires careful reward shaping and integration of the GSPO algorithm into MAS training pipelines.

Limitations Addressed

Prohibitive communication costs in 'free-for-all' multi-agent systems,Lack of explicit optimization for token usage,Scalability challenges in MAS

Performance Gains

Achieves new state-of-the-art performance with a fraction of the token consumption of existing methods.

Technical Tags

Multi-Agent SystemsCommunication EfficiencySequence-Level RLGroup Sequence Policy Optimization (GSPO)Token EconomyReinforcement LearningStrategic SilenceScalable MAS

Research Topics

Multi-Agent Reinforcement LearningCommunication in AIReinforcement Learning AlgorithmsScalable AI Systems

Methods & Architectures

Agent-GSPO FrameworkGroup Sequence Policy Optimization (GSPO)Communication-Aware Reward Multi-Agent Systems (MAS)

Applications & Tasks

Robotics Autonomous Systems Game AI Distributed Computing Communication OverheadScalability of MASEfficient Information Exchange Optimizing token economy in MASTraining agents for communication-efficient interactionAchieving state-of-the-art performance with reduced communication

Datasets & Benchmarks

Benchmarks

Seven reasoning benchmarks

PerformanceToken ConsumptionEmergent Strategies

Related Fields

Game TheoryDistributed SystemsArtificial IntelligenceRobotics

Keywords

Multi-Agent SystemsReinforcement LearningCommunication EfficiencyGSPOToken EconomyScalabilityStrategic SilenceMASSequence RLAgent Communication

Academic Context

#Multi-Agent Reinforcement Learning#Communication in AI#Reinforcement Learning Algorithms#Scalable AI Systems

Technology Stack

Frameworks & Libraries

RLlib

Programming Languages

Python

ML Infrastructure

Distributed Training

Commercial Potential

Potential Products

Efficient MAS platformsCommunication optimization tools for AI agentsFrameworks for scalable multi-robot coordination

Target Industries

RoboticsAutonomous VehiclesLogisticsGamingDefense

Use Case Examples

Coordinating fleets of delivery drones with minimal communicationDeveloping intelligent agents for complex simulationsEnabling efficient collaboration between multiple robots

Competitive Edge

Addresses the critical issue of communication costs in MAS, offering a novel RL-based approach that achieves superior performance with significantly reduced token usage.

Market Opportunity

Growing market for autonomous systems and coordinated AI.

Revenue Models

Licensing of MAS frameworksconsulting services for system design.

Resource Requirements

Compute Needs

High (for multi-agent training)

Data Requirements

Environments suitable for multi-agent interaction and reasoning tasks.

Deployment Constraints

Complexity of training and tuning GSPO for specific MAS scenarios.

Scalability

Explicitly designed for scalability by optimizing communication.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years

Patent Potential

Moderate (novel RL algorithm for MAS)

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