diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index 6f1a1b4ec..bf08c481d 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -93,6 +93,14 @@ check.booster.params <- function(params, ...) {
     interaction_constraints <- sapply(params[['interaction_constraints']], function(x) paste0('[', paste(x, collapse = ','), ']'))
     params[['interaction_constraints']] <- paste0('[', paste(interaction_constraints, collapse = ','), ']')
   }
+
+  # for evaluation metrics, should generate multiple entries per metric
+  if (NROW(params[['eval_metric']]) > 1) {
+    eval_metrics <- as.list(params[["eval_metric"]])
+    names(eval_metrics) <- rep("eval_metric", length(eval_metrics))
+    params_without_ev_metrics <- within(params, rm("eval_metric"))
+    params <- c(params_without_ev_metrics, eval_metrics)
+  }
   return(params)
 }
 
diff --git a/R-package/R/xgb.Booster.R b/R-package/R/xgb.Booster.R
index 37cfc199e..a5c9e5088 100644
--- a/R-package/R/xgb.Booster.R
+++ b/R-package/R/xgb.Booster.R
@@ -697,7 +697,13 @@ xgb.config <- function(object) {
     stop("parameter names cannot be empty strings")
   }
   names(p) <- gsub(".", "_", names(p), fixed = TRUE)
-  p <- lapply(p, function(x) as.character(x)[1])
+  p <- lapply(p, function(x) {
+    if (is.vector(x) && length(x) == 1) {
+      return(as.character(x)[1])
+    } else {
+      return(jsonlite::toJSON(x, auto_unbox = TRUE))
+    }
+  })
   handle <- xgb.get.handle(object)
   for (i in seq_along(p)) {
     .Call(XGBoosterSetParam_R, handle, names(p[i]), p[[i]])
diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R
index 97c1353dc..c96871e11 100644
--- a/R-package/tests/testthat/test_basic.R
+++ b/R-package/tests/testthat/test_basic.R
@@ -566,6 +566,33 @@ test_that("'predict' accepts CSR data", {
   expect_equal(p_csc, p_spv)
 })
 
+test_that("Quantile regression accepts multiple quantiles", {
+  data(mtcars)
+  y <- mtcars[, 1]
+  x <- as.matrix(mtcars[, -1])
+  dm <- xgb.DMatrix(data = x, label = y)
+  model <- xgb.train(
+    data = dm,
+    params = list(
+      objective = "reg:quantileerror",
+      tree_method = "exact",
+      quantile_alpha = c(0.05, 0.5, 0.95),
+      nthread = n_threads
+    ),
+    nrounds = 15
+  )
+  pred <- predict(model, x, reshape = TRUE)
+
+  expect_equal(dim(pred)[1], nrow(x))
+  expect_equal(dim(pred)[2], 3)
+  expect_true(all(pred[, 1] <= pred[, 3]))
+
+  cors <- cor(y, pred)
+  expect_true(cors[2] > cors[1])
+  expect_true(cors[2] > cors[3])
+  expect_true(cors[2] > 0.85)
+})
+
 test_that("Can use multi-output labels with built-in objectives", {
   data("mtcars")
   y <- mtcars$mpg