affine

Affine is an incentivized RL environment which pays miners which make incremental improvements on a set of tasks. The mechanism is sybil-proof, decoy-proof, copy-proof and overfitting-proof.

Affine operates through two participant roles: miners and validators. Miners train AI models using reinforcement learning techniques and deploy them to Chutes (SN64), where they become available for evaluation. Validators then test these models across a set of RL environments covering tasks such as program abduction and coding, measuring how well each model performs relative to the current best.

The evaluation uses a Pareto frontier approach. Validators look for models that dominate across all environments simultaneously, not just excel at a single task. The network applies a winners-take-all mechanism: the model that sits on the Pareto frontier earns the majority of rewards. All other miners are incentivized to download that winning model, improve it through further RL training, and resubmit. This creates a ratchet effect where the network's best model is continuously being refined by competing participants.

The mechanism is designed to resist common attack vectors in decentralized AI. It prevents sybil attacks (creating fake identities), decoy submissions (submitting intentionally weak models to game the system), copy attacks (resubmitting someone else's model without improvement), and overfitting (models that score well on known test cases but fail to generalize). Only models that demonstrate genuine, broad improvements across all evaluation environments are rewarded.

For container orchestration, Affine uses Affinetes, a custom Kubernetes-compatible infrastructure layer that handles environment management, supports both local and remote Docker deployments, and provides environment caching for performance. All evaluation environments are packaged as pre-built Docker images. The project also provides an SDK that allows external developers to evaluate models across different environments programmatically.