News·Opentensor Blog·Jun 11, 2024, 23:07 UTC

Consensus-based Weights

Summary This post describes the new Bittensor feature consensus-based weights. This feature is also known as “liquid alpha” in Bittensor Discord threads. This consensus-based weights feature will be available on the Bittensor testnet (see the Chain release notes for the exact date of the release). With this feature, the dividends to a subnet validator are better correlated to the performance of the subnet miner they are setting the weights on. This feature is made possible by changing the way the exponential moving average for the subnet validator bonds are calculated. See also the release note on the consensus-based weights feature here. Additionally, this feature is designed to amplify the effects of the commit reveal feature that was released recently. Introduction In a Bittensor subnet, the subnet validators assess the work of subnet miners, and set the weights on the subnet miners. These weights determine how to distribute the 41% of subnet emissions, i.e., incentives, earned by the subnet miners. As stated in this Bittensor doc, 18% of subnet’s emission it goes to the subnet owner. 41% goes to subnet validators (this is called dividend). 41% goes to the subnet miners (this is called incentive). This new feature consensus-based weights pertains to the dividends to the subnet validators. To understand the how and why of the changes brought on by the new feature consensus-based weights, below we present how the dividends for a subnet validator are calculated. For a full, mathematically rigorous, explanation of the calculation of dividends, see the section on consensus policy in the Subtensor document for technical details. We compare the weights that each subnet validator sets on each subnet miner, to the average of all subnet validators’ weight settings for that subnet miner. Weights set by an individual subnet validator that are above this average are set to the consensus (average) — a process called clipping. Clipping is used to discourage selfish behavior by

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