Shapes emission distribution curve.
Current Value
10Relevant for: validatorsminersanalyticssubnet owners
The Big Picture
Rho is a parameter in the Yuma consensus mechanism that shapes how emissions are distributed. It affects the steepness of the reward curve - how much top performers earn vs average performers. Higher rho concentrates rewards at the top; lower rho distributes more evenly. It's a lever for tuning competition intensity.
Why This Matters
Rho determines whether it's worth being #1 vs #10 performer. High rho = big gap between winners and runners-up. Low rho = flatter distribution, less winner-take-all.
Example
With high Rho, the top 10% of miners might get 80% of emissions. With low Rho, the top 10% might get 30% of emissions. If you're a mid-tier performer, low Rho subnets are more profitable for you.
Common Questions
- Can subnet owners adjust Rho?
- Yes, it's typically a hyperparameter. Subnets wanting intense competition increase Rho; those wanting broad participation decrease it.
- What's a typical Rho value?
- Varies widely by subnet. Research-focused subnets might use lower Rho for experimentation; production subnets might use higher Rho for quality.
Use Cases
- Emission calculations
- Reward distribution
Code Examples
import { createClient, Binary } from "polkadot-api";
import { getWsProvider } from "polkadot-api/ws";
import { sub } from "@polkadot-api/descriptors"; // generated by: npx papi add sub -w wss://entrypoint-finney.opentensor.ai:443
const client = createClient(getWsProvider("wss://entrypoint-finney.opentensor.ai:443"));
const api = client.getTypedApi(sub);
// Query InitialRho constant
const value = await api.constants.SubtensorModule.InitialRho();
console.log("InitialRho:", value);Type Information
- Type
- u16
- Byte Size
- 2 bytes
- Encoding
- fixed
- Raw Hex
- 0x0a00
Runtime Info
- Pallet
- SubtensorModule
- First Version
- v101
- Latest Version
- v101
- Current Runtime
- v411