[doc] Add more detailed explanations for advanced objectives (#10283)

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Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>

R-package/R/xgb.train.R

@@ -102,6 +102,18 @@
 #'            It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be
 #'            \href{https://en.wikipedia.org/wiki/Tweedie_distribution#Applications}{Tweedie-distributed}.}
 #'   }
+#'
+#'  For custom objectives, one should pass a function taking as input the current predictions (as a numeric
+#'  vector or matrix) and the training data (as an `xgb.DMatrix` object) that will return a list with elements
+#'  `grad` and `hess`, which should be numeric vectors or matrices with number of rows matching to the numbers
+#'  of rows in the training data (same shape as the predictions that are passed as input to the function).
+#'  For multi-valued custom objectives, should have shape `[nrows, ntargets]`. Note that negative values of
+#'  the Hessian will be clipped, so one might consider using the expected Hessian (Fisher information) if the
+#'  objective is non-convex.
+#'
+#'  See the tutorials \href{https://xgboost.readthedocs.io/en/stable/tutorials/custom_metric_obj.html}{
+#'  Custom Objective and Evaluation Metric} and \href{https://xgboost.readthedocs.io/en/stable/tutorials/advanced_custom_obj}{
+#'  Advanced Usage of Custom Objectives} for more information about custom objectives.
 #'  }
 #'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
 #'   \item{ \code{eval_metric} evaluation metrics for validation data.