low error, high CV(RMSE)?
When you calculate RMSE error on multi-output models you will normally end up with 3 RMSE's. You can average those errors into one by specifying weights. They basically reflects of bad error on y1, y2 and y3 are for your application.

Even if the outputs are averaged equally, keep in mind that RMSE is "scale sensitive". If y1 value is way higher than y2 and y3 values, it will also produce bigger RMSE.

If you want to get rid of this problem I recommend you to use RMSE% wich is a relative error metric (scale invariant) as mentionned here : https://stats.stackexchange.com/questions/190868/rmse-is-scale-dependent-is-rmse