diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
index f78b99fbd..754971cfb 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
@@ -1219,8 +1219,10 @@ def fast_beam_search_with_nbest_rescoring(
     temperature: float = 1.0,
 ) -> Dict[str, List[List[int]]]:
     """It limits the maximum number of symbols per frame to 1.
-    A lattice is first obtained using modified beam search, and then
-    the shortest path within the lattice is used as the final output.
+    A lattice is first obtained using fast beam search, num_path are selected
+    and rescored using a given language model. The shortest path within the
+    lattice is used as the final output.
+
     Args:
       model:
         An instance of `Transducer`.
@@ -1366,14 +1368,17 @@ def fast_beam_search_with_nbest_rnn_rescoring(
     sp: spm.SentencePieceProcessor,
     word_table: k2.SymbolTable,
     rnn_lm_model: torch.nn.Module,
+    rnn_lm_scale_list: List[float],
     oov_word: str = "<UNK>",
     use_double_scores: bool = True,
     nbest_scale: float = 0.5,
     temperature: float = 1.0,
 ) -> Dict[str, List[List[int]]]:
     """It limits the maximum number of symbols per frame to 1.
-    A lattice is first obtained using modified beam search, and then
-    the shortest path within the lattice is used as the final output.
+    A lattice is first obtained using fast beam search, num_path are selected
+    and rescored using a given language model and a rnn-lm.
+    The shortest path within the lattice is used as the final output.
+
     Args:
       model:
         An instance of `Transducer`.
@@ -1400,6 +1405,10 @@ def fast_beam_search_with_nbest_rnn_rescoring(
         The BPE model.
       word_table:
         The word symbol table.
+      rnn_lm_model:
+        A rnn-lm model used for LM rescoring
+      rnn_lm_scale_list:
+        A list of floats representing RNN score scales.
       oov_word:
         OOV words are replaced with this word.
       use_double_scores:
@@ -1501,9 +1510,7 @@ def fast_beam_search_with_nbest_rnn_rescoring(
     sos_tokens_row_splits = sos_tokens.shape.row_splits(1)
     sentence_lengths = sos_tokens_row_splits[1:] - sos_tokens_row_splits[:-1]
 
-    x_tokens = sos_tokens.pad(
-        mode="constant", padding_value=model.decoder.blank_id
-    )
+    x_tokens = sos_tokens.pad(mode="constant", padding_value=blank_id)
     y_tokens = tokens_eos.pad(mode="constant", padding_value=blank_id)
 
     x_tokens = x_tokens.to(torch.int64)
@@ -1517,7 +1524,7 @@ def fast_beam_search_with_nbest_rnn_rescoring(
 
     ans: Dict[str, List[List[int]]] = {}
     for n_scale in ngram_lm_scale_list:
-        for rnn_scale in ngram_lm_scale_list:
+        for rnn_scale in rnn_lm_scale_list:
             key = f"ngram_lm_scale_{n_scale}_rnn_lm_scale_{rnn_scale}"
             tot_scores = (
                 am_scores.values
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
index c8d709eba..c3a03f2e1 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
@@ -605,6 +605,7 @@ def decode_one_batch(
             sp=sp,
             word_table=word_table,
             rnn_lm_model=rnn_lm_model,
+            rnn_lm_scale_list=ngram_lm_scale_list,
             use_double_scores=True,
             nbest_scale=params.nbest_scale,
             temperature=params.temperature,
diff --git a/icefall/decode.py b/icefall/decode.py
index 680e29619..e596876f4 100644
--- a/icefall/decode.py
+++ b/icefall/decode.py
@@ -1006,6 +1006,8 @@ def rescore_with_rnn_lm(
         An FsaVec with axes [utt][state][arc].
       num_paths:
         Number of paths to extract from the given lattice for rescoring.
+      rnn_lm_model:
+        A rnn-lm model used for LM rescoring
       model:
         A transformer model. See the class "Transformer" in
         conformer_ctc/transformer.py for its interface.