Patch 1.4.1 - Ranking System Improvements See PR: github.com/VectorChat/chu… This patch addresses two key issues in the current ranking system that made it challenging for newer miners to advance. Issue 1: Identical Chunks, Inconsistent Rewards - OpenAI’s Embedding models are non-deterministic, meaning the same text can generate slightly different embeddings with each submission. - As a result, miners submitting identical chunks could receive slightly different scores, leading to unequal rewards for the same work. Resolution: - Now, if multiple miners submit the same chunk, validators will evaluate the chunk only once, ensuring consistent rewards for all miners submitting identical work. Issue 2: Two Groups, One Ranking. - Many miners are assigned to two groups: one where they can improve their rank and another that could drag their rank down based on the performance of other miners. - Because miner ranks are calculated using a weighted moving average, some miners may experience rank drops despite performing well in their lower group. Example: If a miner ranks 2.7, they could be in both Group 1 (miners ranked 1-4) and Group 2 (ranked 3-8). Even if they consistently rank 1st in Group 2, their overall rank could still fall because 3 is higher than their initial rank of 2.7. Resolution: The starting rank value of each group will now be based on the average of two values: - The rank at the overlapping position in the adjacent higher group. - The last rank of the second adjacent higher group. From this derived starting point, ranks will increment by 1 for each subsequent position, creating a more balanced ranking system. For a more detailed explanation of the issues, solutions, and the logic behind these changes, please refer to our documentation here: github.com/VectorChat/chu…
