diff --git a/paddle/fluid/train/custom_trainer/feed/dataset/data_reader.cc b/paddle/fluid/train/custom_trainer/feed/dataset/data_reader.cc
index ba0a74f1b4c654870e99b9f6bdc43e86964206f3..a84e9467a25c92e119f12f0b52bcfe1f73aa8917 100755
--- a/paddle/fluid/train/custom_trainer/feed/dataset/data_reader.cc
+++ b/paddle/fluid/train/custom_trainer/feed/dataset/data_reader.cc
@@ -87,7 +87,7 @@ public:
         std::string tags;
         std::map<std::string, std::vector<float>> vec_feas;
         int sample_type;
-        std::map<std::string, std::vector<int>> auc_category_info_map; //为细维度计算auc准备的数据
+        std::map<std::string, std::vector<std::string>> auc_category_info_map; //为细维度计算auc准备的数据
         std::vector<FeatureItem> hot_feas, cold_feas; //冷(int32_t)热(uint64_t)feasign
 
         void clear() {
@@ -149,7 +149,7 @@ public:
             return cursor;
         }
 
-        void parse_feas(char* buffer) const {
+        void serialize_to_compress_feas(char* buffer) const {
             if (buffer == nullptr) {
                 return ;
             }
@@ -293,16 +293,21 @@ public:
                     std::string& single_category_str = all_category_vec[i];
                     std::vector<std::string> str_vec = paddle::string::split_string(single_category_str, "=");
                     CHECK(str_vec.size() == 2);
-                    /*std::string category_name = str_vec[0];
-                    std::vector<int> category_info_vec = paddle::string::split_string<int>(str_vec[1], ",");
+                    std::string category_name = str_vec[0];
+                    std::vector<std::string> category_info_vec = paddle::string::split_string<std::string>(str_vec[1], ",");
                     CHECK(category_info_vec.size() > 0);
 
-                    CHECK(rec.auc_category_info_map.insert({category_name, category_info_vec}).second);*/
+                    CHECK(rec.auc_category_info_map.insert({category_name, category_info_vec}).second);
                 }
             } else {
                 uint64_t sign = 0;
                 int slot = -1;
-                CHECK((sign = (uint64_t) strtoull(str, &cursor, 10), cursor != str));
+                sign = (uint64_t)strtoull(str, &cursor, 10);
+                if (cursor == str) { //FIXME abacus没有这种情况
+                    str++;
+                    continue;
+                }
+                //CHECK((sign = (uint64_t)strtoull(str, &cursor, 10), cursor != str));
                 str = cursor;
                 CHECK(*str++ == ':');
                 CHECK(!isspace(*str));
@@ -320,17 +325,12 @@ public:
         }
 
         paddle::framework::BinaryArchive bar;
-        bar << rec.show;
-        bar << rec.clk;
-        bar << rec.tags;
-        bar << rec.vec_feas;
-        bar << rec.sample_type;
-        bar << rec.auc_category_info_map;
-        uint32_t feas_len = rec.calc_compress_feas_lens();
+        bar << rec.show << rec.clk << rec.tags << rec.vec_feas << rec.sample_type << rec.auc_category_info_map;
+        uint32_t feas_len = rec.calc_compress_feas_lens(); //事先计算好压缩后feasign的空间
         bar << feas_len;
         bar.Resize(bar.Length() + feas_len);
-        rec.parse_feas(bar.Cursor());
-        data.data.assign(bar.Buffer(), bar.Length());
+        rec.serialize_to_compress_feas(bar.Finish() - feas_len); //直接在archive内部buffer进行压缩，避免不必要的拷贝
+        data.data.assign(bar.Buffer(), bar.Length()); //TODO 这一步拷贝是否也能避免
 
         return 0;
     }
@@ -355,7 +355,6 @@ public:
 
         paddle::framework::BinaryArchive bar;
         bar.SetReadBuffer(const_cast<char*>(&data.data[0]), data.data.size(), nullptr);
-
         bar >> show;
         bar >> clk;
         bar >> tags;
@@ -363,13 +362,14 @@ public:
         bar >> sample_type;
         bar >> auc_category_info_map;
         bar >> feas_len;
-        parse_feas_to_ins(bar.Cursor(), feas_len, instance.features);
+        CHECK((bar.Finish() - bar.Cursor()) == feas_len);
+        deserialize_feas_to_ins(bar.Cursor(), feas_len, instance.features);
 
         return 0;
     }
 
 private:
-    void parse_feas_to_ins(char* buffer, uint32_t len, std::vector<FeatureItem>& ins) const {
+    void deserialize_feas_to_ins(char* buffer, uint32_t len, std::vector<FeatureItem>& ins) const {
         if (buffer == nullptr) {
             return ;
         }
diff --git a/paddle/fluid/train/custom_trainer/feed/dataset/dataset_container.cc b/paddle/fluid/train/custom_trainer/feed/dataset/dataset_container.cc
old mode 100644
new mode 100755
diff --git a/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc b/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc
old mode 100644
new mode 100755
index cf9ac43b96b50ea4dfa0859d434f6a46992dadf5..71568eade83ca5bad02d72cc09aacc8a0ddd1c4e
--- a/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc
+++ b/paddle/fluid/train/custom_trainer/feed/process/learner_process.cc
@@ -76,7 +76,7 @@ int LearnerProcess::run() {
     uint64_t epoch_id = epoch_accessor->current_epoch_id();
 
     environment->log(EnvironmentRole::WORKER, EnvironmentLogType::MASTER_LOG, EnvironmentLogLevel::NOTICE, 
-        "Resume training with epoch_id:%d label:%s", epoch_id, _context_ptr->epoch_accessor->text(epoch_id).c_str());
+        "Resume train with epoch_id:%d label:%s", epoch_id, _context_ptr->epoch_accessor->text(epoch_id).c_str());
     
     //判断是否先dump出base
     wait_save_model(epoch_id, ModelSaveWay::ModelSaveInferenceBase);