diff --git a/src/learner.cc b/src/learner.cc
index ca6704944..e2187c0ff 100644
--- a/src/learner.cc
+++ b/src/learner.cc
@@ -61,6 +61,8 @@
 #include "xgboost/predictor.h"            // for PredictionContainer, PredictionCacheEntry
 #include "xgboost/string_view.h"          // for operator<<, StringView
 #include "xgboost/task.h"                 // for ObjInfo
+#include <iostream>
+#include <exception>
 
 namespace {
 const char* kMaxDeltaStepDefaultValue = "0.7";
@@ -1262,34 +1264,57 @@ class LearnerImpl : public LearnerIO {
     return out_impl;
   }
 
-  void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) override {
-    monitor_.Start("UpdateOneIter");
-    TrainingObserver::Instance().Update(iter);
-    this->Configure();
-    this->InitBaseScore(train.get());
+void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) override {
+  std::cerr << "Entering UpdateOneIter, iteration: " << iter << std::endl;
+  monitor_.Start("UpdateOneIter");
+  TrainingObserver::Instance().Update(iter);
+  
+  std::cerr << "Configuring..." << std::endl;
+  this->Configure();
+  
+  std::cerr << "Initializing base score..." << std::endl;
+  this->InitBaseScore(train.get());
 
-    if (ctx_.seed_per_iteration) {
-      common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
-    }
-
-    this->ValidateDMatrix(train.get(), true);
-
-    auto& predt = prediction_container_.Cache(train, ctx_.Device());
-
-    monitor_.Start("PredictRaw");
-    this->PredictRaw(train.get(), &predt, true, 0, 0);
-    TrainingObserver::Instance().Observe(predt.predictions, "Predictions");
-    monitor_.Stop("PredictRaw");
-
-    monitor_.Start("GetGradient");
-    GetGradient(predt.predictions, train->Info(), iter, &gpair_);
-    monitor_.Stop("GetGradient");
-    TrainingObserver::Instance().Observe(*gpair_.Data(), "Gradients");
-
-    gbm_->DoBoost(train.get(), &gpair_, &predt, obj_.get());
-    monitor_.Stop("UpdateOneIter");
+  if (ctx_.seed_per_iteration) {
+    std::cerr << "Setting seed for iteration..." << std::endl;
+    common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
   }
 
+  std::cerr << "Validating DMatrix..." << std::endl;
+  this->ValidateDMatrix(train.get(), true);
+
+  std::cerr << "Caching predictions..." << std::endl;
+  auto& predt = prediction_container_.Cache(train, ctx_.Device());
+
+  monitor_.Start("PredictRaw");
+  std::cerr << "Predicting raw values..." << std::endl;
+  this->PredictRaw(train.get(), &predt, true, 0, 0);
+  TrainingObserver::Instance().Observe(predt.predictions, "Predictions");
+  monitor_.Stop("PredictRaw");
+
+  monitor_.Start("GetGradient");
+  std::cerr << "Getting gradients..." << std::endl;
+  try {
+    GetGradient(predt.predictions, train->Info(), iter, &gpair_);
+  } catch (const std::exception& e) {
+    std::cerr << "Exception in GetGradient: " << e.what() << std::endl;
+    throw;
+  }
+  monitor_.Stop("GetGradient");
+  TrainingObserver::Instance().Observe(*gpair_.Data(), "Gradients");
+
+  std::cerr << "Performing boosting..." << std::endl;
+  try {
+    gbm_->DoBoost(train.get(), &gpair_, &predt, obj_.get());
+  } catch (const std::exception& e) {
+    std::cerr << "Exception in DoBoost: " << e.what() << std::endl;
+    throw;
+  }
+  
+  monitor_.Stop("UpdateOneIter");
+  std::cerr << "Exiting UpdateOneIter" << std::endl;
+}
+
   void BoostOneIter(int iter, std::shared_ptr<DMatrix> train,
                     linalg::Matrix<GradientPair>* in_gpair) override {
     monitor_.Start("BoostOneIter");