# 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](/flock-tokenomics/network-participation/ai-arena/training-node.md), 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](/flock-tokenomics/network-participation/ai-arena/validator.md). $$s\_d$$ 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 0.[^1]4, the training node stakes 3,000 and the delegator delegate 1,000. Consider $$\epsilon$$ to be 1 in this example. 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.

[^1]: here is 0.4?


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