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Introducing CycleQD: A population-based model merging via Quality Diversity CycleQD builds on our model merging research, advancing two fronts: evolving a swarm of specialized agents to complement one another, and laying the groundwork for life-long learning by enabling diverse, adaptable skill acquisition at the population-level.

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Sakana AI

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121,513 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 8

Фото профиля Sakana AI
Sakana AI1 год назад

Please check out our paper, Agent Skill Acquisition for Large Language Models via CycleQD This work aims to mimic an ecological niche during the training of a swarm of LLM agents. Just like how each species in an environment finds its own role and position, or niche, a well-evolved AI agent doesn’t have to be great at everything, but it can effectively occupy a specific niche, making it resilient to competition from other agents in the swarm. This kind of approach can enable a population of AI agents to emerge, each with specific capabilities that complement each other, collectively improving over time. The core idea in CycleQD is to create an artificial evolutionary process in which Model Merging is used as a cross-over operation, SVD as a mutation operation, and Quality Diversity as the selection operation, encouraging each agent in the population to develop its own unique capabilities which adds value to the collective. In the paper, we show that CycleQD is able to evolve a swarm of LLM agents, each with their own niche, to tackle difficult agentic workflow tasks. We believe that the future of AI lies in life-long learning where collective systems continuously grow, adapt, and accumulate knowledge over time. CycleQD is a first step, enabling diverse skill learning as a foundation for continual learning.

Фото профиля TuringPost
TuringPost1 год назад

This is very interesting! Can we say, that you use a swarm intelligence concept here?

Фото профиля Brandon
Brandon1 год назад

Seems pretty reasonable to me

Фото профиля baraa tulip
baraa tulip1 год назад

@ceobillionaire 💥💥💢💢💥💥💢💥 please please Help Btc : bc1qv0xceh6h4eaawhnqq95nty85r02vpgjzrjyrjg Eth : 0xaeac98A1a3a3f260Ce969fB57C4ab0595f51f113

Фото профиля justboulatbek
justboulatbek1 год назад

Are these things available for self deploy to try? Or as a service?

Фото профиля AI Carlos
AI Carlos1 год назад

I'm intrigued by CycleQD's potential for life-long learning. Can it adapt to new tasks without requiring extensive retraining?

Фото профиля Belkhir Nacim
Belkhir Nacim1 год назад

any idea to investigate differential evolution or an ES strategy in lieu of a swarm approach?

Фото профиля Data & Analytics
Data & Analytics1 год назад

@hardmaru @hardmaru, cycleQD sounds like an intriguing concept! Merging models with Quality Diversity could shake things up in AI research. What aspects of it grab your attention?

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