Training Node

Training nodes are responsible for training and fine-tuning the AI tasks initiated by the task creators. This mechanism ensures the integrity and health of the ecosystem, as nodes have vested interests via staking. In return, the nodes will be rewarded in proportion to their contributions. To become a training node, a user has to stake FLAI.

0. Overview: reward drivers for training nodes

Put simply, a training node’s daily return from a task depends on three factors:

(1) the relative staking amount of this task against all tasks, meaning a training node’s stake in a particular task will indirectly affect it’s rewards from that task; and

(2) a training node’s stake in this task as well as stake delegated to this training node; and

(3) quality of the node’s submission, as shown by the node’s relative ranking. Specifically, it is a geometric series, along with its ranking, multiplied by the relative stake of this task.

It’s important to note that rank is being used here to determine the quality of the training node’s work, not other metrics such as absolute scores, primarily because scores can come very closely between nodes. Such design decision is believed to make reward calculation fairer and easier.