https://github.com/the-swarm-corporation/modelarena

ModelArena: A Competitive Environment for Multi-Agent Training
https://github.com/the-swarm-corporation/modelarena

agents attention llms models multi-agent multi-agent-collaboration multi-llm research ssm transformers

Last synced: 3 months ago
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ModelArena: A Competitive Environment for Multi-Agent Training

• Host: GitHub
• URL: https://github.com/the-swarm-corporation/modelarena
• Owner: The-Swarm-Corporation
• License: mit
• Created: 2025-03-05T22:09:57.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-03-05T22:35:18.000Z (3 months ago)
• Last Synced: 2025-03-05T23:21:55.834Z (3 months ago)
• Topics: agents, attention, llms, models, multi-agent, multi-agent-collaboration, multi-llm, research, ssm, transformers
• Language: Python
• Homepage: https://swarms.ai
• Size: 305 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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README

        
# ModelArena: A Competitive Environment for Multi-Agent Training

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We introduce ModelArena (A Competitive En- vironment for Multi-Agent Training), a novel training methodology that dynamically real- locates computational resources across multi- ple models during simultaneous training. Un- like conventional approaches that train mod- els in isolation or with static resource alloca- tion, ModelArena creates a competitive learn- ing environment where models that demon- strate faster learning rates are dynamically re- warded with increased memory allocation. This introduces a selection mechanism inspired by evolutionary principles, where computational resources flow toward models exhibiting the most promising learning trajectories. We for- mulate the mathematical foundation for mea- suring relative learning rates, implement an adaptive memory reallocation strategy, and demonstrate its effectiveness across heteroge- neous model architectures. Our experiments with transformer-based language models show that ModelArena can efficiently identify and pri- oritize high-potential models, leading to more effective resource utilization and accelerated training for the most promising architectures. Additionally, we discuss the implications of this approach for multi-agent systems and pro- pose extensions for collaborative-competitive training regimes that could further enhance model development. The method introduces a new training paradigm that combines principles from meta-learning, neural architecture search, and evolutionary computation into a unified framework for model training optimization.

# Todo

- [ ] Fix the table in figure 7 page 7

- [ ] Reduce equations

- [ ] Add more references

- [ ] Add more charts and graphs to the evaluations

- [ ] Run another experiment with the llama3 7b and mistral