Controls how quickly difficulty responds to network conditions.
Current Value
0The Big Picture
Difficulty adjustment uses an exponential moving average to smooth changes. Alpha controls how much weight is given to recent registration rates vs historical averages. High alpha = fast response to demand changes; low alpha = slow, stable adjustments. This prevents both oscillation and sluggish adaptation to changing network conditions.
Why This Matters
If you're monitoring difficulty for registration timing, alpha tells you how predictable difficulty changes are. High alpha = volatile; low alpha = stable and forecastable.
Example
With AdjustmentAlpha of 0.1, difficulty changes slowly. If registrations spike, difficulty ramps up gradually over many intervals. With alpha of 0.9, difficulty would jump quickly in response to the same spike.
Common Questions
- What's a typical alpha value?
- Usually low (0.1-0.3) to prevent wild swings. Stable difficulty helps miners plan hardware investments.
- Can subnet owners adjust this?
- It depends on subnet hyperparameter configuration. Network-wide defaults can be overridden per subnet.
Use Cases
- Difficulty adjustment algorithm
Code Examples
import { ApiPromise, WsProvider } from "@polkadot/api";
import { stringCamelCase } from "@polkadot/util";
const provider = new WsProvider("wss://entrypoint-finney.opentensor.ai:443");
const api = await ApiPromise.create({ provider });
// Query InitialAdjustmentAlpha constant
const value = api.consts[stringCamelCase("SubtensorModule")][stringCamelCase("InitialAdjustmentAlpha")];
console.log("InitialAdjustmentAlpha:", value.toHuman());
// Convert RAO to TAO if this is a balance value
const taoValue = value.toBigInt() / BigInt(1e9);
console.log("TAO:", taoValue.toString());Type Information
- Type
- u64
- Byte Size
- 8 bytes
- Encoding
- fixed
- Raw Hex
- 0x0000000000000000
Runtime Info
- Pallet
- SubtensorModule
- First Version
- v126
- Latest Version
- v126
- Current Runtime
- v393