diff --git a/plugin/example/custom_obj.cc b/plugin/example/custom_obj.cc
index 615aa058a540..df831e7fa26d 100644
--- a/plugin/example/custom_obj.cc
+++ b/plugin/example/custom_obj.cc
@@ -77,7 +77,7 @@ class MyLogistic : public ObjFunction {
   }
 
   void Load(Json const& in) override {
-    param_.InitAllowUnknown(fromJson(get<Object>(in["MyLogisticParam"])));
+    fromJson(in["MyLogisticParam"], &param_);
   }
 
  private:
diff --git a/src/learner.cc b/src/learner.cc
index 650c58085c6c..33be08ee283d 100644
--- a/src/learner.cc
+++ b/src/learner.cc
@@ -407,12 +407,6 @@ class LearnerImpl : public Learner {
       metrics[i] = String(metrics_[i]->Name());
     }
     learner["metrics"] = Array(metrics);
-
-    std::map<std::string, Json> j_cfg;
-    for (auto const& kv : cfg_) {
-      j_cfg[kv.first] = kv.second;
-    }
-    learner["cfg_cache"] = Object(std::move(j_cfg));
   }
 
   void Load(Json const& in) override {
@@ -442,11 +436,6 @@ class LearnerImpl : public Learner {
       metric_names_[i]= get<String>(j_metrics[i]);
       metrics_[i] = std::unique_ptr<Metric>(Metric::Create(metric_names_.back(), &generic_param_));
     }
-
-    auto j_cfg = get<Object const>(learner.at("cfg_cache"));
-    for (auto const& kv : j_cfg) {
-      cfg_[kv.first] = get<String const>(kv.second);
-    }
   }
 
   // rabit save model to rabit checkpoint
diff --git a/tests/cpp/gbm/test_gbtree.cc b/tests/cpp/gbm/test_gbtree.cc
index bd4c46c7930a..b7dfd2f35e90 100644
--- a/tests/cpp/gbm/test_gbtree.cc
+++ b/tests/cpp/gbm/test_gbtree.cc
@@ -45,7 +45,7 @@ TEST(GBTree, SelectTreeMethod) {
                     {"num_feature", n_feat}});
   ASSERT_EQ(tparam.updater, "grow_gpu_hist");
   ASSERT_EQ(tparam.predictor, "gpu_predictor");
-#endif
+#endif  // XGBOOST_USE_CUDA
 }
 
 #ifdef XGBOOST_USE_CUDA
@@ -109,9 +109,7 @@ TEST(GBTree, ChoosePredictor) {
   // data is not pulled back into host
   ASSERT_FALSE(data.HostCanWrite());
 }
-<<<<<<< HEAD
-#endif
-=======
+#endif  // XGBOOST_USE_CUDA
 
 // Some other parts of test are in `Tree.Json_IO'.
 TEST(GBTree, Json_IO) {
@@ -159,6 +157,4 @@ TEST(Dart, Json_IO) {
   auto j_weight_drop = get<Array>(model["weight_drop"]);
   ASSERT_EQ(j_weight_drop.size(), 1);  // One tree is trained.
 }
-
->>>>>>> Add JSON IO for various components.
 }  // namespace xgboost