diff --git a/Schafkopf.Training/Algos/MDP.cs b/Schafkopf.Training/Algos/MDP.cs
index 49658cd..083be7c 100644
--- a/Schafkopf.Training/Algos/MDP.cs
+++ b/Schafkopf.Training/Algos/MDP.cs
@@ -32,14 +32,13 @@ public void Collect(PPORolloutBuffer buffer)
 
         for (int gameId = 0; gameId < numGames + 1; gameId++)
         {
-            Console.Write($"\rcollecting ppo training data { gameId+1 } / { numGames+1 } ...        ");
+            Console.Write($"\rcollecting data { gameId+1 } / { numGames+1 }        ");
             playGame(envs, states, batchesOfTurns);
             prepareRewards(states, rewards);
             fillBuffer(gameId, buffer, states, batchesOfTurns, rewards);
             for (int i = 0; i < states.Length; i++)
                 states[i] = envs[i].Reset();
         }
-
         Console.WriteLine();
     }
 
diff --git a/Schafkopf.Training/Algos/PPOAgent.cs b/Schafkopf.Training/Algos/PPOAgent.cs
index 4e9e791..9ee2809 100644
--- a/Schafkopf.Training/Algos/PPOAgent.cs
+++ b/Schafkopf.Training/Algos/PPOAgent.cs
@@ -38,16 +38,16 @@ public PPOModel Train(PPOTrainingSettings config)
 
         for (int ep = 0; ep < config.NumTrainings; ep++)
         {
+            Console.WriteLine($"epoch {ep+1}");
             exps.Collect(rollout);
             model.Train(rollout);
 
-            if ((ep + 1) % 10 == 0)
-            {
-                model.RecompileCache(batchSize: 1);
-                double winRate = benchmark.Benchmark(agent);
-                model.RecompileCache(batchSize: config.BatchSize);
-                Console.WriteLine($"epoch {ep}: win rate vs. random agents is {winRate}");
-            }
+            model.RecompileCache(batchSize: 1);
+            double winRate = benchmark.Benchmark(agent);
+            model.RecompileCache(batchSize: config.BatchSize);
+
+            Console.WriteLine($"win rate vs. random agents: {winRate}");
+            Console.WriteLine("--------------------------------------");
         }
 
         return model;
@@ -150,7 +150,7 @@ public void Train(PPORolloutBuffer memory)
         int i = 1;
         foreach (var batch in batches)
         {
-            Console.Write($"\rtraining {i++} / {numBatches}");
+            Console.Write($"\rtraining {i++} / {numBatches}           ");
             updateModels(batch);
         }
         Console.WriteLine();
diff --git a/Schafkopf.Training/Benchmark/RandomPlayBenchmark.cs b/Schafkopf.Training/Benchmark/RandomPlayBenchmark.cs
index 51c3a08..13afd5b 100644
--- a/Schafkopf.Training/Benchmark/RandomPlayBenchmark.cs
+++ b/Schafkopf.Training/Benchmark/RandomPlayBenchmark.cs
@@ -21,6 +21,7 @@ public double Benchmark(ISchafkopfAIAgent agentToEval, int epochs = 10_000)
         int wins = 0;
         for (int i = 0; i < epochs; i++)
         {
+            Console.Write($"\rbenchmarking {i+1} / {epochs}          ");
             var log = session.ProcessGame();
 
             // info: only evaluate games where cards were played
@@ -31,6 +32,7 @@ public double Benchmark(ISchafkopfAIAgent agentToEval, int epochs = 10_000)
             bool isWin = !eval.DidCallerWin ^ isCaller;
             wins += isWin ? 1 : 0;
         }
+        Console.WriteLine();
 
         return (double)wins / epochs; // win rate
     }