diff --git a/README.md b/README.md
index 2cef7adb..a9b1f75c 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,7 @@ Currently, on NVIDIA L20, RTX 4090 and RTX 3080 Laptop, compared with cuBLAS's d
 |✔️|✔️|✔️|✔️|
 
 
-I have also implemented **FlashAttention-2** using pure MMA PTX instructions, which supports features such as Multi-Stages, Tile MMA, Tile Warp, Shared KV SMEM, **Fully Shared QKV SMEM**, **Prefetch Q s2r**, Collective Store, etc. Please refer to [flash-attention-mma⚡️⚡️](./kernels/flash-attn) for more details.
+I have also implemented **FlashAttention-2** using pure MMA PTX instructions, which supports features such as Multi-Stages, Tile MMA, Tile Warp, Shared KV SMEM, **Fully Shared QKV SMEM**, **Prefetch Q s2r**, **Prefetch K/V g2s**, **QK Fine-grained Tiling**, Collective Store, etc. Please refer to [flash-attention-mma⚡️⚡️](./kernels/flash-attn) for more details.
 
 ![flash-attn-mma](https://github.com/user-attachments/assets/6f66796d-44d5-4ec1-b224-af997bd152b2)
 
diff --git a/kernels/flash-attn/mma/flash_attn_mma_tiling_qk.cu b/kernels/flash-attn/mma/flash_attn_mma_tiling_qk.cu
index b57b24c5..0de6d278 100644
--- a/kernels/flash-attn/mma/flash_attn_mma_tiling_qk.cu
+++ b/kernels/flash-attn/mma/flash_attn_mma_tiling_qk.cu
@@ -45,7 +45,8 @@
 // Fine-grained tiling at the MMA level for Q and K results in a constant SRAM usage of
 // 64 * kMmaAtomK for Q and K. For V, the SRAM complexity is O(kMmaAtomK * d), leading to
 // an overall SRAM complexity of O(kMmaAtomK * d). Consequently, this approach allows us to
-// extend D (head dimension) up to 1024. Performance is stay tuned for updates ~
+// extend D (head dimension) up to 1024. Performance optimizations are ongoing. 
+// Stay tuned for updates ~
 
 template<
          const int kHeadDim,          // Headdim, 32,64,128