diff --git a/R/racing_helpers.R b/R/racing_helpers.R
index 448fbdf..be3a283 100644
--- a/R/racing_helpers.R
+++ b/R/racing_helpers.R
@@ -393,6 +393,9 @@ restore_rset <- function(x, index) {
   att <- attributes(x)
   x <- x |> dplyr::slice(index)
   att$row.names <- att$row.names[index]
+  if (!is.null(att[[".resample_weights"]])) {
+    att[[".resample_weights"]] <- att[[".resample_weights"]][index]
+  }
   attributes(x) <- att
   x
 }
@@ -633,6 +636,7 @@ randomize_resamples <- function(x) {
   att <- attributes(x)
   B <- nrow(x)
   x$.rand <- runif(B)
+  x$.orig_order <- seq_len(B)
   reps <- attr(x, "repeats")
   if (!is.null(reps) && reps > 1) {
     x <- x |>
@@ -641,8 +645,13 @@ randomize_resamples <- function(x) {
   } else {
     x <- x |> dplyr::arrange(.rand)
   }
+  new_order <- x$.orig_order
   x$.rand <- NULL
+  x$.orig_order <- NULL
   att$row.names <- att$row.names
+  if (!is.null(att[[".resample_weights"]])) {
+    att[[".resample_weights"]] <- att[[".resample_weights"]][new_order]
+  }
   attributes(x) <- att
   x
 }
diff --git a/tests/testthat/test-resample-weights.R b/tests/testthat/test-resample-weights.R
new file mode 100644
index 0000000..078c03c
--- /dev/null
+++ b/tests/testthat/test-resample-weights.R
@@ -0,0 +1,172 @@
+test_that("restore_rset subsets .resample_weights", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weights <- c(0.5, 1.0, 1.5, 2.0, 2.5)
+  attr(folds, ".resample_weights") <- weights
+
+  result <- finetune:::restore_rset(folds, 1:3)
+
+  expect_length(attr(result, ".resample_weights"), 3)
+  expect_equal(attr(result, ".resample_weights"), weights[1:3])
+})
+
+test_that("restore_rset single-row subset returns single weight", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weights <- c(0.5, 1.0, 1.5, 2.0, 2.5)
+  attr(folds, ".resample_weights") <- weights
+
+  result <- finetune:::restore_rset(folds, 2)
+
+  expect_length(attr(result, ".resample_weights"), 1)
+  expect_equal(attr(result, ".resample_weights"), 1.0)
+})
+
+test_that("restore_rset non-contiguous index maps weights correctly", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weights <- c(0.5, 1.0, 1.5, 2.0, 2.5)
+  attr(folds, ".resample_weights") <- weights
+
+  result <- finetune:::restore_rset(folds, c(1, 3, 5))
+
+  expect_length(attr(result, ".resample_weights"), 3)
+  expect_equal(attr(result, ".resample_weights"), c(0.5, 1.5, 2.5))
+})
+
+test_that("restore_rset full index preserves all weights", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weights <- c(0.5, 1.0, 1.5, 2.0, 2.5)
+  attr(folds, ".resample_weights") <- weights
+
+  result <- finetune:::restore_rset(folds, 1:5)
+
+  expect_equal(attr(result, ".resample_weights"), weights)
+})
+
+test_that("restore_rset without weights keeps NULL", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+
+  result <- finetune:::restore_rset(folds, 1:3)
+
+  expect_null(attr(result, ".resample_weights"))
+  expect_equal(nrow(result), 3)
+})
+
+test_that("randomize_resamples reorders weights to match rows", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weights <- seq_len(nrow(folds)) * 10
+  attr(folds, ".resample_weights") <- weights
+
+  split_ids_before <- purrr::map_chr(folds$splits, ~ .x$id$id)
+
+  set.seed(42)
+  result <- finetune:::randomize_resamples(folds)
+
+  split_ids_after <- purrr::map_chr(result$splits, ~ .x$id$id)
+  result_weights <- attr(result, ".resample_weights")
+
+  expect_length(result_weights, nrow(folds))
+
+  for (i in seq_len(nrow(result))) {
+    orig_pos <- which(split_ids_before == split_ids_after[i])
+    expect_equal(result_weights[i], weights[orig_pos])
+  }
+})
+
+test_that("randomize_resamples with repeated CV reorders weights", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 3, repeats = 2)
+  weights <- seq_len(nrow(folds)) * 10
+  attr(folds, ".resample_weights") <- weights
+
+  id_pairs_before <- paste(folds$id, folds$id2)
+
+  set.seed(42)
+  result <- finetune:::randomize_resamples(folds)
+
+  id_pairs_after <- paste(result$id, result$id2)
+  result_weights <- attr(result, ".resample_weights")
+
+  expect_length(result_weights, nrow(folds))
+
+  for (i in seq_len(nrow(result))) {
+    orig_pos <- which(id_pairs_before == id_pairs_after[i])
+    expect_equal(result_weights[i], weights[orig_pos])
+  }
+})
+
+test_that("randomize_resamples without weights keeps NULL, no column leaks", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  orig_names <- names(folds)
+
+  set.seed(42)
+  result <- finetune:::randomize_resamples(folds)
+
+  expect_null(attr(result, ".resample_weights"))
+  expect_equal(names(result), orig_names)
+  expect_false(".orig_order" %in% names(result))
+  expect_false(".rand" %in% names(result))
+})
+
+test_that("randomize then restore composes correctly", {
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weights <- c(10, 20, 30, 40, 50)
+  attr(folds, ".resample_weights") <- weights
+
+  split_ids_orig <- purrr::map_chr(folds$splits, ~ .x$id$id)
+
+  set.seed(42)
+  randomized <- finetune:::randomize_resamples(folds)
+  subset_idx <- 1:3
+  restored <- finetune:::restore_rset(randomized, subset_idx)
+
+  split_ids_restored <- purrr::map_chr(restored$splits, ~ .x$id$id)
+  restored_weights <- attr(restored, ".resample_weights")
+
+  expect_length(restored_weights, 3)
+
+  for (i in seq_along(split_ids_restored)) {
+    orig_pos <- which(split_ids_orig == split_ids_restored[i])
+    expect_equal(restored_weights[i], weights[orig_pos])
+  }
+})
+
+test_that("tune_race_anova works with weighted resamples", {
+  skip_if_not(
+    exists("add_resample_weights", where = asNamespace("tune"), inherits = FALSE),
+    message = "tune::add_resample_weights not available"
+  )
+
+  set.seed(1)
+  folds <- rsample::vfold_cv(mtcars, v = 5)
+  weighted_folds <- tune::add_resample_weights(folds, rep(1, nrow(folds)))
+
+  spec <- parsnip::decision_tree(
+    cost_complexity = parsnip::tune(),
+    min_n = parsnip::tune()
+  ) |>
+    parsnip::set_mode("regression") |>
+    parsnip::set_engine("rpart")
+
+  wflow <- workflows::workflow() |>
+    workflows::add_model(spec) |>
+    workflows::add_formula(mpg ~ .)
+
+  grid <- expand.grid(cost_complexity = c(0.001, 0.01, 0.1), min_n = c(2, 5))
+
+  expect_no_error({
+    set.seed(42)
+    result <- tune_race_anova(
+      wflow,
+      resamples = weighted_folds,
+      grid = grid,
+      control = control_race(verbose_elim = FALSE, verbose = FALSE)
+    )
+  })
+})