fix(logging): correct multi-rank "max" metric reduction

accelerate.reduce only implements sum/mean (max silently returned the
SUM across ranks, inflating max-reduced metrics by num_processes). Gather
per-rank values and reduce explicitly for max/sum/mean.

Co-authored-by: Cursor <cursoragent@cursor.com>

src/lerobot/utils/logging_utils.py

@@ -176,10 +176,21 @@ class MetricsTracker:
         if not buckets:
             return
 
+        # NB: don't use ``accelerator.reduce(..., reduction="max")`` — accelerate only implements
+        # "sum"/"mean" (it always all-reduces with SUM and divides for "mean"), so "max" silently
+        # returns the SUM across ranks, inflating every "max" metric by ``num_processes`` (e.g. a
+        # 3.5s step reported as 28s on 8 GPUs). Gather per-rank values and reduce them explicitly.
         device = self.accelerator.device
+        num_processes = self.accelerator.num_processes
         for reduction, names in buckets.items():
-            tensor = torch.tensor([self.metrics[n].avg for n in names], dtype=torch.float32, device=device)
-            reduced = self.accelerator.reduce(tensor, reduction=reduction)
+            local = torch.tensor([self.metrics[n].avg for n in names], dtype=torch.float32, device=device)
+            gathered = self.accelerator.gather(local).view(num_processes, len(names))
+            if reduction == "max":
+                reduced = gathered.amax(dim=0)
+            elif reduction == "sum":
+                reduced = gathered.sum(dim=0)
+            else:  # "mean"
+                reduced = gathered.mean(dim=0)
             for name, value in zip(names, reduced.tolist(), strict=True):
                 meter = self.metrics[name]
                 # Preserve avg == sum / count so a later .update() on this meter accumulates