diff --git a/pufferlib/pufferl.py b/pufferlib/pufferl.py
index 14a270defd..fe691be4e3 100644
--- a/pufferlib/pufferl.py
+++ b/pufferlib/pufferl.py
@@ -292,6 +292,7 @@ def __init__(self, config, vecenv, policy, logger=None):
         self.vecenv = vecenv
         self.epoch = 0
         self.global_step = 0
+        self.agent_steps = 0
         self.last_log_step = 0
         self.last_log_time = time.time()
         self.start_time = time.time()
@@ -548,7 +549,7 @@ def train(self):
                 policy=self.uncompiled_policy,
                 env_name=self.config["env"],
                 logger=self.logger,
-                global_step=self.global_step,
+                global_step=self.agent_steps,
             )
 
         return logs
@@ -814,6 +815,7 @@ def mean_and_log(self):
 
         device = config["device"]
         agent_steps = int(dist_sum(self.global_step, device))
+        self.agent_steps = agent_steps
         logs = {
             "SPS": dist_sum(self.sps, device),
             "agent_steps": agent_steps,
@@ -1437,17 +1439,22 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None, early_stop
         model.forward_eval = policy.forward_eval
         policy = model.to(local_rank)
 
-    if args["neptune"]:
-        logger = NeptuneLogger(args)
-    elif args["wandb"]:
-        logger = WandbLogger(args)
-    elif args["tb"]:
-        date_time = datetime.now().strftime("%Y%m%d-%H%M%S")
-        experiment_dir = os.path.join(args["train"]["data_dir"], rf"{env_name}_" + date_time)
-        logger = TensorBoardLogger(
-            run_id=date_time,
-            experiment_dir=experiment_dir,
-        )
+    # Under DDP only rank 0 owns the run logger; other ranks keep logger=None,
+    # which PuffeRL wraps in a NoLogger. Without this gate every rank calls
+    # wandb.init()/NeptuneLogger and you get world_size duplicate runs.
+    is_rank0 = (not torch.distributed.is_initialized()) or torch.distributed.get_rank() == 0
+    if is_rank0:
+        if args["neptune"]:
+            logger = NeptuneLogger(args)
+        elif args["wandb"]:
+            logger = WandbLogger(args)
+        elif args["tb"]:
+            date_time = datetime.now().strftime("%Y%m%d-%H%M%S")
+            experiment_dir = os.path.join(args["train"]["data_dir"], rf"{env_name}_" + date_time)
+            logger = TensorBoardLogger(
+                run_id=date_time,
+                experiment_dir=experiment_dir,
+            )
 
     train_config = dict(**args["train"], env=env_name, eval=args.get("eval", {}))
     pufferl = PuffeRL(train_config, vecenv, policy, logger)
@@ -1546,7 +1553,7 @@ def train(env_name, args=None, vecenv=None, policy=None, logger=None, early_stop
             policy=pufferl.uncompiled_policy,
             env_name=pufferl.config["env"],
             logger=pufferl.logger,
-            global_step=pufferl.global_step,
+            global_step=pufferl.agent_steps,
             force=True,
         )