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 pay a registration fee and stake $FML.

For each task, the rewards for training nodes will be divided into two parts, reward A (which is daily emitted) and reward B (which is vested and emitted when the task is finished).

• During one day, consider the situation where there are $M$ active AI Arena tasks with the total staking amounts of $(S_1, ..., S_M)$, $\delta$ and $\beta$ are the parameters that we can adjust via the decision of FLock DAO. In version 1.0, these parameters are set as: $\delta= 0.1$ and $\beta = 1$.

• Thus, assuming:

  • $M$ is the number of tasks in total

  • $E$ is the daily emission of $FML

  • $S_i$ is the total stake amount of a particular task

  • $C$ is the score that a particular training node receive in accordance with its relative rank against all other nodes for the same task, as well as the training node’s stake in the task

  *Note that apart from $C$, all other computations in the reward calculation is done on-chain.

• For a given AI Arena task with the total staking amount of $S_i$, its daily total rewards is:

$$E \cdot \frac{S^\beta_i}{\sum_{k=1}^{M}{S^\beta_k}}$$

• Consider the total stake of training nodes is the task is $S_{tn}$ and the total stake of validators (and their delegators) is the task is $S_{vd}$. And $\omega$ is a system parameter (which is initially set as 1). Then daily rewards for training nodes are:

$$E \cdot \frac{S^\beta_i}{\sum_{k=1}^{M}{S^\beta_k}} \cdot \frac{S_{tn}^\omega}{S_{tn}^\omega+S_{vd}^\omega} \cdot C$$

• Specifically, Reward A for a given training node is:

$$E \cdot \frac{S^\beta_i}{\sum_{k=1}^{M}{S^\beta_k}} \cdot \frac{S_{tn}^\omega}{S_{tn}^\omega+S_{vd}^\omega} \cdot \delta \cdot C$$

• And Reward B for the same given training node will be:

$$E \cdot \frac{S^\beta_i}{\sum_{k=1}^{M}{S^\beta_k}} \cdot \frac{S_{tn}^\omega}{S_{tn}^\omega+S_{vd}^\omega} \cdot (1-\delta) \cdot C$$

• And, assuming $D_i$ refers to the duration of a task its task yield (or sum of daily returns) is:

$$y_t = \frac{r_t}{s_t}\cdot D_i \cdot 100 \%$$

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; 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.

Example

Let’s say the total stake of all tasks for a particular day is 2,450, daily emission is 1,074, there are 3 training nodes and 1 validator for task A. Consider node a, b, and c each staked 100, 200, 300, and they rank first, second and third respectively. The validator staked 500, meaning total stakes for this task is 1,100 .

Imagine there are also tasks B and C, and their total stakes are 500 and 850 respectively. In this example, we only illustrate the reward distribution for task A. We further assume that C, the score from off-chain calculation is 0.3886.

Thus, daily reward for node a who staked 100 and ranked first is:

$$\begin{align*}r_t^a &= 1074 \cdot \frac{1100}{2450} \cdot 0.1667 \cdot 0.3886 \\r_t^a &\approx 31.38\end{align*}$$

Assuming task duration is 30 days, task yields for this node are:

$$\begin{align*}y_t^a &= \frac{31.38}{100} \cdot 30 \\y_t^a &\approx 941.4\%\end{align*}$$