diff --git a/tests/jax/test_permutation.py b/tests/jax/test_permutation.py
index 43f2553eed..5bb59c6ed5 100644
--- a/tests/jax/test_permutation.py
+++ b/tests/jax/test_permutation.py
@@ -19,11 +19,6 @@
 from utils import assert_allclose, pytest_parametrize_wrapper
 
 
-# =============================================================================
-# Test parameter definitions with L0 (fast) and L2 (comprehensive) levels
-# =============================================================================
-
-# All dispatch/combine test cases
 ALL_DISPATCH_COMBINE_CASES = [
     (128, 5, 128, 3),
     (1024, 8, 128, 8),
@@ -35,7 +30,6 @@
     "L2": ALL_DISPATCH_COMBINE_CASES,
 }
 
-# All sort chunks test cases
 ALL_SORT_CHUNKS_CASES = [
     (8, 4096, 1280),
     (64, 4096, 4096),
@@ -46,7 +40,6 @@
     "L2": ALL_SORT_CHUNKS_CASES,
 }
 
-# All dispatch/combine with padding test cases
 ALL_DISPATCH_COMBINE_PADDING_CASES = [
     (128, 5, 128, 3, 8),
     (1024, 8, 128, 8, 16),
@@ -58,14 +51,12 @@
     "L2": ALL_DISPATCH_COMBINE_PADDING_CASES,
 }
 
-# Dtypes for testing
 ALL_DTYPES = [jnp.float32, jnp.bfloat16]
 DTYPES = {
     "L0": ALL_DTYPES,
     "L2": ALL_DTYPES,
 }
 
-# With probs options
 ALL_WITH_PROBS = [True, False]
 WITH_PROBS = {
     "L0": [True],
@@ -97,7 +88,9 @@ def reference_make_row_id_map(
 
     # Compute total tokens per expert and expert offsets
     tokens_per_expert = jnp.sum(routing_map, axis=0)
-    expert_offsets = jnp.concatenate([jnp.array([0]), jnp.cumsum(tokens_per_expert)[:-1]])
+    expert_offsets = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(tokens_per_expert)[:-1].astype(jnp.int32)]
+    )
 
     # Compute destination rows for all (token, expert) pairs
     # dest_row[i, j] = expert_offsets[j] + cumsum_per_expert[i, j] - 1 if routed, else -1
@@ -115,7 +108,9 @@ def reference_make_row_id_map(
 
     # Gather the sorted destination rows and expert indices using advanced indexing
     # Create indices for gathering
-    token_idx = jnp.broadcast_to(jnp.arange(num_tokens)[:, None], (num_tokens, num_experts))
+    token_idx = jnp.broadcast_to(
+        jnp.arange(num_tokens, dtype=jnp.int32)[:, None], (num_tokens, num_experts)
+    )
     sorted_dest_rows = dest_rows_all[token_idx, sorted_expert_indices]
 
     # Build row_id_map: [dest_row_0, ..., dest_row_{E-1}, expert_idx_0, ..., expert_idx_{E-1}, n_routed]
@@ -373,23 +368,27 @@ def reference_make_chunk_sort_map(
         Row ID map for chunk sorting of shape [num_tokens,].
     """
     # Compute source chunk boundaries (cumulative sum of original split_sizes)
-    src_cumsum = jnp.concatenate([jnp.array([0]), jnp.cumsum(split_sizes)])
+    src_cumsum = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(split_sizes).astype(jnp.int32)]
+    )
 
     # Compute destination chunk boundaries based on sorted order
     sorted_sizes = split_sizes[sorted_indices]
-    dest_cumsum = jnp.concatenate([jnp.array([0]), jnp.cumsum(sorted_sizes)])
+    dest_cumsum = jnp.concatenate(
+        [jnp.array([0], dtype=jnp.int32), jnp.cumsum(sorted_sizes).astype(jnp.int32)]
+    )
 
     # For each source chunk, compute its destination offset
     # inverse_indices[i] = position of chunk i in sorted order
-    inverse_indices = jnp.argsort(sorted_indices)
+    inverse_indices = jnp.argsort(sorted_indices).astype(jnp.int32)
     dest_offsets = dest_cumsum[inverse_indices]
 
     # Create row_id_map: for each token position, compute its destination
     # First, figure out which chunk each position belongs to
-    position_indices = jnp.arange(num_tokens)
+    position_indices = jnp.arange(num_tokens, dtype=jnp.int32)
 
     # chunk_ids[i] = which chunk position i belongs to
-    chunk_ids = jnp.searchsorted(src_cumsum[1:], position_indices, side="right")
+    chunk_ids = jnp.searchsorted(src_cumsum[1:], position_indices, side="right").astype(jnp.int32)
 
     # within_chunk_offset[i] = position i's offset within its chunk
     within_chunk_offset = position_indices - src_cumsum[chunk_ids]