# Train to Earn #### Our system uses data-driven techniques to map and interpret cryptonative activities, and its performance will be impacted by the breadth and quality of training data that is used in our primary ML models and fine-tuned LLM's. #### In order to maximize the impact of our algorithms, we will enlist the help of our power-users and motivated cryptonative researchers who seek to mutually benefit from improving the algorithm's quality and timeliness at providing relevant information. #### Since 2022, OzDAO has benefited from gathering quality ML training data from a small group of its own power users that became motivated by the prospect of improving the product they are using, showing that a positive feedback loop can generate quality data and results for a decentralized, data-driven organization. #### WandBot will take this further with incentivizing a larger group of power users to submit data feedback and labels designed with maximizing the performance of our underlying data-driven models in a train-to-earn system, by rewarding data labelers proportionally to the quality of their contribution. Primarily, the feedback will be used in fine-tuning our LLM Agent Layer. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.wandbot.app/yellow-brick-roadmap/train-to-earn.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.