diff --git a/shardy/dialect/sdy/transforms/propagation/op_sharding_rule_registry.cc b/shardy/dialect/sdy/transforms/propagation/op_sharding_rule_registry.cc
index 12f8d127d..3441a13a0 100644
--- a/shardy/dialect/sdy/transforms/propagation/op_sharding_rule_registry.cc
+++ b/shardy/dialect/sdy/transforms/propagation/op_sharding_rule_registry.cc
@@ -449,10 +449,18 @@ OpShardingRuleAttr createOpShardingRule(Operation* op,
         if (!conservativePropagation) {
           // Only add a factor for spatial dimensions if we are not in
           // conservative mode.
-          for (auto [lhsDim, rhsDim, outDim] :
-               llvm::zip_equal(dimNums.getInputSpatialDimensions(),
-                               dimNums.getKernelSpatialDimensions(),
-                               dimNums.getOutputSpatialDimensions())) {
+          std::optional<ArrayRef<bool>> windowReversal =
+              conv.getWindowReversal();
+          for (auto [i, dims] : llvm::enumerate(
+                   llvm::zip_equal(dimNums.getInputSpatialDimensions(),
+                                   dimNums.getKernelSpatialDimensions(),
+                                   dimNums.getOutputSpatialDimensions()))) {
+            if (windowReversal.has_value() && (*windowReversal)[i]) {
+              // TODO(b/396724444). Add support for the reversed dimensions when
+              // we support it in the mesh.
+              continue;
+            }
+            const auto& [lhsDim, rhsDim, outDim] = dims;
             // The input spatial dimension can be sharded along either the
             // number of windows (corresponds to the output spatial dimension)
             // or the window size (corresponds to the kernel spatial dimension),
@@ -1067,14 +1075,19 @@ OpShardingRuleAttr createOpShardingRule(Operation* op,
         return builder.build();
       })
       .Case<stablehlo::ReverseOp>([](stablehlo::ReverseOp reverse) {
-        std::function<FactorType(int64_t)> getFactorType = [&](int64_t dim) {
-          return llvm::is_contained(reverse.getDimensions(), dim)
-                     ? FactorType::kPermutation
-                     : FactorType::kPassThrough;
-        };
-        return OpShardingRuleBuilder(reverse)
-            .addPointwise(getTensorShape(reverse.getResult()), getFactorType)
-            .build();
+        OpShardingRuleBuilder builder(reverse);
+        for (const auto& [dim, dimSize] :
+             llvm::enumerate(getTensorShape(reverse.getResult()))) {
+          if (llvm::is_contained(reverse.getDimensions(), dim)) {
+            // TODO(b/396724444). Add support for the reversed dimensions when
+            // we support it in the mesh.
+            builder.addFactor(dim, dimSize, FactorType::kPermutation,
+                              /*isBlocked=*/true);
+          } else {
+            builder.addFactor(dim, dimSize);
+          }
+        }
+        return builder.build();
       })
       .Case<stablehlo::RngBitGeneratorOp>(
           [](stablehlo::RngBitGeneratorOp rngBitGenerator) {
diff --git a/shardy/dialect/sdy/transforms/propagation/test/op_sharding_rule_registry.mlir b/shardy/dialect/sdy/transforms/propagation/test/op_sharding_rule_registry.mlir
index 5c3870f52..845f4edc8 100644
--- a/shardy/dialect/sdy/transforms/propagation/test/op_sharding_rule_registry.mlir
+++ b/shardy/dialect/sdy/transforms/propagation/test/op_sharding_rule_registry.mlir
@@ -215,14 +215,15 @@ func.func @concat_not_all_operands_are_from_slices_of_the_same_tensor(%arg0: ten
 
 // CHECK-LABEL: func @conv_simple
 func.func @conv_simple(%arg0 : tensor<2x224x224x192xf32>, %arg1 : tensor<3x3x192x64xf32>) -> tensor<2x112x112x64xf32> {
-  // CHECK: sdy.sharding_rule = #sdy.op_sharding_rule<([i, jk, lm, n], [k, m, n, o])->([i, j, l, o]) {i=2, j=112, k=2, l=112, m=2, n=192, o=64} reduction={k, m, n} permutation={j, l}>
+  // CHECK: sdy.sharding_rule = #sdy.op_sharding_rule<([i, n, jk, l], [o, k, l, m])->([i, p, j, m]) {i=2, j=112, k=2, l=192, m=64, n=1, o=1, p=1} reduction={k, l} permutation={j}>
   %0 = stablehlo.convolution(%arg0, %arg1)
     dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
     window = {stride = [2, 2], pad = [[0, 1], [0, 1]]} {
       batch_group_count = 1 : i64,
       feature_group_count = 1 : i64,
       lhs_dilations = dense<1> : tensor<2xi64>,
-      rhs_dilations = dense<1> : tensor<2xi64>
+      rhs_dilations = dense<1> : tensor<2xi64>,
+      window_reversal = array<i1: true, false>
     } : (tensor<2x224x224x192xf32>, tensor<3x3x192x64xf32>) -> tensor<2x112x112x64xf32>
   return %0 : tensor<2x112x112x64xf32>
 }
@@ -896,7 +897,7 @@ func.func @reshape_split_dim_with_intermediate_one(%arg0: tensor<32xf32>) -> ten
 
 // CHECK-LABEL: func @reverse
 func.func @reverse(%arg0: tensor<4x32x8x2xf32>) -> tensor<4x32x8x2xf32> {
-  // CHECK: sdy.sharding_rule = #sdy.op_sharding_rule<([i, j, k, l])->([i, j, k, l]) {i=4, j=32, k=8, l=2} permutation={j, l}>
+  // CHECK: sdy.sharding_rule = #sdy.op_sharding_rule<([i, j, k, l])->([i, j, k, l]) {i=4, j=32, k=8, l=2} permutation={j, l} blocked_propagation={j, l}>
   %0 = stablehlo.reverse %arg0, dims = [1, 3] : tensor<4x32x8x2xf32>
   return %0 : tensor<4x32x8x2xf32>
 }