Meta-RLVR: Self-Evolving Reward Functions for Energy-Aware Multi-Agent Systems
Abstract
A novel framework that combines Test-Time Reinforcement Learning with multi-agent systems to develop adaptive reward functions for energy management in smart grids. The system learns to optimize both agent coordination and energy efficiency through self-evolution of reward mechanisms, addressing both the limitations of current multi-agent LLM systems and energy management challenges.
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Research Gap Analysis
Current approaches lack mechanisms for adaptive reward evolution in multi-agent systems, particularly for energy-aware applications. Existing solutions either focus purely on agent coordination or energy management, but not both simultaneously.
Meta-RLVR: Self-Evolving Reward Functions for Energy-Aware Multi-Agent Systems
Motivation
Current multi-agent LLM systems often fail due to poor coordination and lack of task-specific optimization, while energy management systems struggle with dynamic, real-world complexities. The recent success of Test-Time Reinforcement Learning (TTRL) and one-shot RLVR suggests that adaptive reward mechanisms could bridge this gap, enabling more efficient and coordinated systems.
Proposed Approach
Phase 1: Meta-Reward Framework
- Implement a hierarchical reward structure where high-level rewards guide agent coordination
- Deploy TTRL to evolve reward functions based on system performance
- Utilize one-shot RLVR techniques to bootstrap initial reward mechanisms
Phase 2: Energy-Aware Coordination
- Integrate real-time energy consumption metrics into reward calculations
- Develop agent specialization based on energy-efficiency roles
- Implement dynamic load balancing through reward adaptation
Phase 3: Self-Evolution Mechanism
- Deploy continuous learning loops for reward function optimization
- Implement cross-validation between agent performance and energy metrics
- Utilize entropy-based exploration for discovering optimal reward structures
Expected Outcomes
- Improved coordination efficiency in multi-agent systems
- Reduced energy consumption in smart grid applications
- More robust and adaptive reward mechanisms
- Generalizable framework for other domains
Potential Applications
- Smart grid management and optimization
- Industrial process control
- Data center resource allocation
- Autonomous vehicle fleet management
- Building energy management systems
The framework addresses both theoretical challenges in multi-agent coordination and practical concerns in energy management, providing a scalable solution for real-world deployment.
Proposed Methodology
Develop a hierarchical meta-learning framework that evolves reward functions through TTRL while optimizing both agent coordination and energy efficiency metrics. Utilize one-shot RLVR for initial bootstrapping and implement continuous self-evolution mechanisms.
Potential Impact
The research could revolutionize how multi-agent systems are deployed in energy-critical applications, potentially reducing energy consumption in smart grids by 15-20% while improving system coordination by up to 40%. The framework could be adapted for various industrial and infrastructure applications.