The Gensyn Public Testnet brings persistent identity to decentralised AI systems and provides a network to track participation, maintain attribution, make payments, coordinate remote execution, verify untrusted operations, log decentralised training runs, crowd-fund large-scale training efforts, and more.
The network is a custom Ethereum Rollup dedicated to machine learning and integrated with off-chain execution, verification, and communication frameworks.
Phase 0
The first phase of the Testnet focuses on tracking participation within RL Swarm, our application for collaborative post-training via reinforcement learning reasoning over the internet. Connecting an RL Swarm node to an on-chain identity provides a consistent view of individual node contributions.
Get involved
- Swarm Participants: Run a node, improve the overall intelligence of the swarm, track your participation, and train your own local model to use as you wish.
- Developers: Train a local model by participating in the swarm, deploy in local applications without being beholden to an external company, and continually update its parameters in a living, decentralised system.
- ML Researchers: Deploy your own swarm for others to join, train on new datasets, solve new problems, construct new objectives, incentivise participation and explore the space of decentralised AI with an entirely new infrastructure stack.
- Community Members: Stay up to date on progress, provide feedback, and discuss future developments in the Discord.
Architecture
The network is a custom Ethereum Rollup dedicated to machine learning and integrated with off-chain execution, verification, and communication frameworks.
Roadmap
The Testnet will follow a phased rollout, with each phase introducing new features derived from the infrastructure that Gensyn builds. The Testnet will allow the community to stress test the protocol under real-world conditions and provide feedback on features and direction during the rollout. As phases progress, more applications will become available - covering the full ML lifecycle from pre-training through inference.
The final phase will culminate in the Mainnet launch, with real economic value transacted via the chain.