diff --git a/egs/swbd/s5b/RESULTS b/egs/swbd/s5b/RESULTS
index 51dc417d4..e196c578e 100644
--- a/egs/swbd/s5b/RESULTS
+++ b/egs/swbd/s5b/RESULTS
@@ -99,6 +99,15 @@ for x in exp/{mono,tri,sgmm,nnet}*/decode*; do [ -d $x ] && grep WER $x/wer_* |
 %WER 17.9 | 1831 21395 | 83.8 10.8 5.5 1.7 17.9 55.8 | exp/nnet6a_gpu/decode_eval2000_sw1_fsh_tgpr/score_13/eval2000.ctm.swbd.filt.sys
 %WER 18.4 | 1831 21395 | 83.6 11.4 5.0 2.1 18.4 56.1 | exp/nnet6a_gpu/decode_eval2000_sw1_tg/score_11/eval2000.ctm.swbd.filt.sys
 
+# Discriminative training on top of 5c (14.6 still not as Good as Karel's 14.1 number with this LM, but getting closer).
+%WER 15.0 | 1831 21395 | 87.3 9.0 3.7 2.3 15.0 52.2 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_fsh_tgpr_epoch1/score_15/eval2000.ctm.swbd.filt.sys
+%WER 14.8 | 1831 21395 | 87.3 8.9 3.8 2.2 14.8 51.9 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_fsh_tgpr_epoch2/score_16/eval2000.ctm.swbd.filt.sys
+%WER 14.7 | 1831 21395 | 87.5 8.9 3.6 2.2 14.7 51.7 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_fsh_tgpr_epoch3/score_16/eval2000.ctm.swbd.filt.sys
+%WER 14.6 | 1831 21395 | 87.6 8.9 3.6 2.2 14.6 51.5 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_fsh_tgpr_epoch4/score_16/eval2000.ctm.swbd.filt.sys
+%WER 15.2 | 1831 21395 | 87.1 9.1 3.8 2.3 15.2 53.1 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_tg_epoch1/score_15/eval2000.ctm.swbd.filt.sys
+%WER 15.1 | 1831 21395 | 87.4 9.1 3.5 2.5 15.1 52.6 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_tg_epoch2/score_13/eval2000.ctm.swbd.filt.sys
+%WER 15.0 | 1831 21395 | 87.5 9.0 3.5 2.5 15.0 52.4 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_tg_epoch3/score_14/eval2000.ctm.swbd.filt.sys
+%WER 14.9 | 1831 21395 | 87.4 9.1 3.5 2.4 14.9 52.3 | exp/nnet6c_mpe_gpu/decode_eval2000_sw1_tg_epoch4/score_15/eval2000.ctm.swbd.filt.sys
 
 
 
@@ -113,5 +122,3 @@ for x in exp/{mono,tri,sgmm,nnet}*/decode*; do [ -d $x ] && grep WER $x/wer_* |
 # Final system rescored by sw1_fsh trigram (unpruned)
 %WER 13.4 | 1831 21395 | 88.4 8.2 3.4 1.8 13.4 49.2 | exp/tri4b_pretrain-dbn_dnn_smbr_iter1-lats/decode_eval2000_sw1_fsh_tg.3_it2/score_14/eval2000.ctm.swbd.filt.sys
 
-
-
diff --git a/egs/swbd/s5b/local/nnet2/run_6c_gpu.sh b/egs/swbd/s5b/local/nnet2/run_6c_gpu.sh
index f2a56d777..735510dfc 100755
--- a/egs/swbd/s5b/local/nnet2/run_6c_gpu.sh
+++ b/egs/swbd/s5b/local/nnet2/run_6c_gpu.sh
@@ -28,24 +28,12 @@ set -e # exit on error.
 # likely generate very thin lattices.  Note: the transform-dir is important to
 # specify, since this system is on top of fMLLR features.
 
-nj=$(cat exp/tri4b/num_jobs)
 
-if [ $stage -le 0 ]; then
-  steps/nnet2/make_denlats.sh --cmd "$decode_cmd -l mem_free=1G,ram_free=1G" \
-    --nj $nj --sub-split 20 --num-threads 6 --parallel-opts "-pe smp 6" \
-    --transform-dir exp/tri4b \
-    data/train_nodup data/lang exp/nnet5c_gpu exp/nnet5c_gpu_denlats
-fi
-
-if [ $stage -le 1 ]; then
-  steps/nnet2/align.sh  --cmd "$decode_cmd $gpu_opts" --use-gpu yes \
-    --transform-dir exp/tri4b \
-    --nj $nj data/train_nodup data/lang exp/nnet5c_gpu exp/nnet5c_gpu_ali
-fi
 
 if [ $stage -le 2 ]; then
   steps/nnet2/train_discriminative.sh --cmd "$decode_cmd"  --learning-rate 0.000002 \
-    --num-epochs 2 \
+    --modify-learning-rates true --last-layer-factor 0.1 \
+    --num-epochs 4 --cleanup false \
     --num-jobs-nnet 4 --stage $train_stage \
     --transform-dir exp/tri4b \
     --num-threads 1 --parallel-opts "$gpu_opts" data/train data/lang \
@@ -53,7 +41,7 @@ if [ $stage -le 2 ]; then
 fi
 
 if [ $stage -le 3 ]; then
-  for epoch in 1 2; do
+  for epoch in 1 2 3 4; do 
     for lm_suffix in tg fsh_tgpr; do
       steps/nnet2/decode.sh --cmd "$decode_cmd" --nj 30 --iter epoch$epoch \
         --config conf/decode.config --transform-dir exp/tri4b/decode_eval2000_sw1_${lm_suffix} \
@@ -63,5 +51,5 @@ if [ $stage -le 3 ]; then
 fi
 
 
-exit 0;
 
+exit 0;
diff --git a/egs/wsj/s5/steps/nnet2/train_discriminative.sh b/egs/wsj/s5/steps/nnet2/train_discriminative.sh
index d22b8f223..9f04a0a90 100755
--- a/egs/wsj/s5/steps/nnet2/train_discriminative.sh
+++ b/egs/wsj/s5/steps/nnet2/train_discriminative.sh
@@ -22,7 +22,8 @@ num_jobs_nnet=4    # Number of neural net jobs to run in parallel.  Note: this
 samples_per_iter=400000 # measured in frames, not in "examples"
 
 spk_vecs_dir=
-
+modify_learning_rates=false
+last_layer_factor=1.0 # relates to modify-learning-rates
 shuffle_buffer_size=5000 # This "buffer_size" variable controls randomization of the samples
                 # on each iter.  You could set it to 0 or to a large value for complete
                 # randomization, but this would both consume memory and cause spikes in
@@ -78,6 +79,8 @@ if [ $# != 6 ]; then
   echo "                                                   # the middle."
   echo "  --criterion <criterion|smbr>                     # Training criterion: may be smbr, mmi or mpfe"
   echo "  --boost <boost|0.0>                              # Boosting factor for MMI (e.g., 0.1)"
+  echo "  --modify-learning-rates <true,false|false>       # If true, modify learning rates to try to equalize relative"
+  echo "                                                   # changes across layers."
   echo "  --degs-dir <dir|"">                              # Directory for discriminative examples, e.g. exp/foo/degs"
   exit 1;
 fi
@@ -283,6 +286,11 @@ while [ $x -lt $num_iters ]; do
     $cmd $dir/log/average.$x.log \
       nnet-am-average $nnets_list $dir/$[$x+1].mdl || exit 1;
 
+    if $modify_learning_rates; then
+      $cmd $dir/log/modify_learning_rates.$x.log \
+        nnet-modify-learning-rates --last-layer-factor=$last_layer_factor $dir/$x.mdl \
+           $dir/$[$x+1].mdl $dir/$[$x+1].mdl
+    fi
     rm $nnets_list
   fi