diff --git a/README.md b/README.md
index 298feca2e..51c0cee32 100644
--- a/README.md
+++ b/README.md
@@ -39,9 +39,10 @@ and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc].
 
 The best WER we currently have is:
 
-||test-clean|test-other|
-|--|--|--|
-|WER| 2.57% | 5.94% |
+|     | test-clean | test-other |
+|-----|------------|------------|
+| WER | 2.42       | 5.73       |
+
 
 We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
 
@@ -49,9 +50,9 @@ We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In
 
 The WER for this model is:
 
-||test-clean|test-other|
-|--|--|--|
-|WER| 6.59% | 17.69% |
+|     | test-clean | test-other |
+|-----|------------|------------|
+| WER | 6.59       | 17.69      |
 
 We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model:  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing)
 
diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md
index d7229f6b0..8d7c867c0 100644
--- a/egs/librispeech/ASR/RESULTS.md
+++ b/egs/librispeech/ASR/RESULTS.md
@@ -33,7 +33,8 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
   --world-size 4 \
   --bucketing-sampler 1 \
   --start-epoch 0 \
-  --num-epochs 80
+  --num-epochs 90
+# Note: It trains for 90 epochs, but the best WER is at epoch-77.pt
 ```
 
 and the following command for decoding
@@ -55,6 +56,9 @@ and the following command for decoding
 You can find the pre-trained model by visiting
 <https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09>
 
+The tensorboard log for training is available at
+<https://tensorboard.dev/experiment/hZDWrZfaSqOMqtW0NEfXKg/#scalars>
+
 
 #### 2021-08-19
 (Wei Kang): Result of https://github.com/k2-fsa/icefall/pull/13