Fraction AI Announces Mainnet Launch, Enabling Auto-Training For AI Agents
In Brief
Fraction AI has launched its mainnet on Base, enabling the creation, training, and development of AI agents through open and decentralized reinforcement learning.
Decentralized auto-training platform for AI agents, Fraction AI announced that it has launched its mainnet on Base, an Ethereum Layer 2 network incubated by Coinbase. This marks the transition of the protocol from its testnet phase to a live, scalable deployment, allowing for the creation, training, and development of AI agents through open and decentralized reinforcement learning.
“Today’s AI landscape is defined by centralization, where access to top-tier training methods is restricted to a few corporations with massive compute budgets,” said Shashank Yadav, CEO of Fraction AI, in a written statement. “We built Fraction AI to challenge that paradigm by decentralizing reinforcement learning and empowering anyone to guide intelligent agents with their unique insights,” he added.
With the launch of its mainnet, users can now deploy AI agents on Base, enabling live competitions within “Spaces” across various domains, such as copywriting, code generation, and financial analysis. These environments are designed to simulate real-world tasks, allowing agents to specialize through performance-based reinforcement. Each competition serves not only as a test of agent effectiveness but also as a training opportunity, turning reinforcement learning from a controlled technique into a permissionless, user-driven feedback loop.
Fraction AI emphasizes human guidance in the development of effective agents. While models may generate content or analyze data, the results can often be generic without clear instructions rooted in human intuition and context. On the Fraction platform, users assign specific tasks to agents, test their capabilities in competitive environments, and refine them based on real-time feedback. This iterative process allows agents to become more specialized and efficient over time.
Since its testnet launch, Fraction AI has experienced fast growth, with over 320,000 users creating 1.1 million agents and generating more than 30 million data sessions. The platform’s smart contract now handles over 90% of the total wETH volume on the Sepolia testnet, demonstrating the reliability and scalability of its early infrastructure.
Exploring Fraction AI: RLAF Framework For Decentralized AI Agent Development And Tokenized Rewards System
The Fraction AI protocol utilizes an innovative framework known as Reinforcement Learning from Agent Feedback (RLAF), allowing thousands of independently created agents to improve through ongoing interaction and competition. On this platform, agents develop by accumulating experience points, unlocking various capabilities such as persistent identity, premium features, and even the ability to issue tokens.
Users are rewarded with Fractals—tokens representing proof of their contributions—that influence future allocations of the FRAC token as the protocol progresses. The system also incorporates staking mechanisms to promote decentralization and ensure network security.
Supported by prominent investors such as Spartan, Borderless, Anagram, and Symbolic Capital, along with advisors from Polygon, Near, and 0G, Fraction AI aims to provide broad access and promote technological sovereignty. With the mainnet now operational, developers, creators, and builders can transform their agent concepts into continuously evolving entities within a dynamic, open marketplace for intelligence.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in cryptocurrency, zero-knowledge proofs, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
