Delegator

Delegators are responsible for delegating stake to training nodes and/ or validators.

0. Overview: reward drivers for delegators

Reward for the delegator depends on:

1. quality of the training nodes/ validators selected, as measured by the scores/ ranks training nodes/ validators received based on the quality of their work

2. amount of stake delegator delegated

1. Reward distribution for delegators

If we consider $f_i$ to the the rewards distributed to training nodes and their delegators as explained here, then the reward for delegator to this given training node is:

$$f_i \cdot (1-\sigma)\cdot\tfrac{t_d}{t_n + t_d}$$

Here, $t_d$ refers to the amount the delegator delegated to the given training node, whereas $t_n$ is the stake amount from the same training node.

Similarly, if a delegator delegated FLCOK to a validator, the reward for this delegator is:

$$f_i\cdot (1 - \sigma) \cdot \frac{s_d}{s_v+S_d}$$

Here, $f_i$ refers to the rewards earned by validators and their delegators as explained here. $s_d$ is the delegated amount from delegator to this validator, whereas $s_v$ refers to the stake amount from the validator.

Note that in the front-end, you will see a “reward-sharing ratio”, which refers to $(1 - \sigma)$, which means when reward-sharing ratio is 60%, $\sigma$ is 0.4. This ratio is set by training nodes and validators permissionlessly.

2. Example

Let's assume rewards for a given training node and its delegator ( $f_i)$ is 58,084, $\sigma$ to be 4, the training node stakes 3,000 and the delegator delegate 1,000. The reward for the delegator alone (excluding that to the training node) is:

$$f_i \cdot (1-\sigma)\cdot\tfrac{t_d}{t_n + t_d} = 58,084 \times \Bigl(0.6 \times \tfrac{1000}{3000 + 1000}\Bigr) \;\approx\; 8{,}712.6$$

Note that rewards in delegation pools are time-weighted to balance fairness for long-term participants and incentivize new delegations. As pools grow, rewards stabilize, promoting sustained engagement. Also, delegators must maintain their stake for at least 24 hours before un-delegating. This ensures meaningful contributions and prevents exploitative behaviors.

Parameters like reward splits ( $\gamma$) are fine-tuned through DAO voting. This democratized control keeps the ecosystem adaptive and equitable.