diff --git a/egs/wsj/s5/steps/libs/nnet3/train/common.py b/egs/wsj/s5/steps/libs/nnet3/train/common.py
index d052c78b3f8..a2892a090f3 100644
--- a/egs/wsj/s5/steps/libs/nnet3/train/common.py
+++ b/egs/wsj/s5/steps/libs/nnet3/train/common.py
@@ -69,9 +69,12 @@ def get_multitask_egs_opts(egs_dir, egs_prefix="",
         '--output=ark:foo/egs/output.3.ark --weight=ark:foo/egs/weights.3.ark'
         i.e. egs_prefix is "" for train and
         "valid_diagnostic." for validation.
+
+        Caution: archive_index is usually an integer, but may be a string ("JOB")
+        in some cases.
     """
     multitask_egs_opts = ""
-    egs_suffix = ".{0}".format(archive_index) if archive_index > -1 else ""
+    egs_suffix = ".{0}".format(archive_index) if archive_index != -1 else ""
 
     if use_multitask_egs:
         output_file_name = ("{egs_dir}/{egs_prefix}output{egs_suffix}.ark"
diff --git a/src/rnnlm/rnnlm-example-utils.cc b/src/rnnlm/rnnlm-example-utils.cc
index 5aa2465d24d..a019012d20e 100644
--- a/src/rnnlm/rnnlm-example-utils.cc
+++ b/src/rnnlm/rnnlm-example-utils.cc
@@ -290,7 +290,7 @@ static void ProcessRnnlmOutputNoSampling(
     row_sums.ApplyLog();
     BaseFloat ans = -VecVec(row_sums, minibatch.output_weights);
     *objf_den_exact =  ans;
-    if (fabs(ans) > 100) {
+    if (fabs(ans) > 1.0 * nnet_output.NumRows()) {
       KALDI_WARN << "Big den objf "  << ans;
     }
   }