diff --git a/docs/tracking.md b/docs/tracking.md
index d993fa4da..1d4fa07e3 100644
--- a/docs/tracking.md
+++ b/docs/tracking.md
@@ -68,3 +68,9 @@ You can parse a group (with other traversal tasks) by running:
 ```sh
 $ parse_tc_group <task_group_id>
 ```
+
+### Extend supported Marian metrics
+
+The parser does not supports arbitrary metrics (i.e. via the `--valid-metrics` argument).
+
+In order to support new values, you may want to update the regular expression matching Marian output lines in `tracking.translations_parser.parser` and the dataclass in `tracking.translations_parser.data`.
diff --git a/tests/data/experiments_wandb_calls_1_10.json b/tests/data/experiments_wandb_calls_1_10.json
index 4c24afeef..8497ce25f 100644
--- a/tests/data/experiments_wandb_calls_1_10.json
+++ b/tests/data/experiments_wandb_calls_1_10.json
@@ -1 +1 @@
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diff --git a/tests/data/experiments_wandb_calls_1_12.json b/tests/data/experiments_wandb_calls_1_12.json
index b1e699877..088bb083e 100644
--- a/tests/data/experiments_wandb_calls_1_12.json
+++ b/tests/data/experiments_wandb_calls_1_12.json
@@ -1,30752 +1 @@
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diff --git a/tests/data/taskcluster_wandb_calls.json b/tests/data/taskcluster_wandb_calls.json
index 758368650..7051d1c4f 100644
--- a/tests/data/taskcluster_wandb_calls.json
+++ b/tests/data/taskcluster_wandb_calls.json
@@ -1 +1 @@
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"data": {"ce_mean_words_stalled": 10.0}}, {"step": 69000, "data": {"bleu_detok_stalled": 12.0}}, {"step": 70000, "data": {"epoch": 1}}, {"step": 70000, "data": {"sen": 53623225}}, {"step": 70000, "data": {"cost": 3.61772156}}, {"step": 70000, "data": {"time": 667.63}}, {"step": 70000, "data": {"rate": 26308.74}}, {"step": 70000, "data": {"gnorm": 0.7542}}, {"step": 71000, "data": {"epoch": 1}}, {"step": 71000, "data": {"sen": 54396474}}, {"step": 71000, "data": {"cost": 3.59983325}}, {"step": 71000, "data": {"time": 324.87}}, {"step": 71000, "data": {"rate": 54889.64}}, {"step": 71000, "data": {"gnorm": 0.7614}}, {"step": 72000, "data": {"epoch": 1}}, {"step": 72000, "data": {"sen": 55162764}}, {"step": 72000, "data": {"cost": 3.58433032}}, {"step": 72000, "data": {"time": 264.69}}, {"step": 72000, "data": {"rate": 67080.17}}, {"step": 72000, "data": {"gnorm": 0.7056}}, {"step": 72000, "data": {"epoch": 1}}, {"step": 72000, "data": {"chrf": 25.439}}, {"step": 72000, "data": {"ce_mean_words": 3.17615}}, {"step": 72000, "data": {"bleu_detok": 2.38534}}, {"step": 72000, "data": {"chrf_stalled": 10.0}}, {"step": 72000, "data": {"ce_mean_words_stalled": 11.0}}, {"step": 72000, "data": {"bleu_detok_stalled": 13.0}}, {"step": 73000, "data": {"epoch": 1}}, {"step": 73000, "data": {"sen": 55934767}}, {"step": 73000, "data": {"cost": 3.57955575}}, {"step": 73000, "data": {"time": 678.16}}, {"step": 73000, "data": {"rate": 26378.77}}, {"step": 73000, "data": {"gnorm": 0.6862}}, {"step": 74000, "data": {"epoch": 1}}, {"step": 74000, "data": {"sen": 56707046}}, {"step": 74000, "data": {"cost": 3.58926153}}, {"step": 74000, "data": {"time": 263.9}}, {"step": 74000, "data": {"rate": 67178.94}}, {"step": 74000, "data": {"gnorm": 0.6927}}, {"step": 75000, "data": {"epoch": 1}}, {"step": 75000, "data": {"sen": 57452767}}, {"step": 75000, "data": {"cost": 3.5871098}}, {"step": 75000, "data": {"time": 265.53}}, {"step": 75000, "data": {"rate": 66803.51}}, {"step": 75000, "data": {"gnorm": 0.6945}}, {"step": 75000, "data": {"epoch": 1}}, {"step": 75000, "data": {"chrf": 25.1609}}, {"step": 75000, "data": {"ce_mean_words": 3.20571}}, {"step": 75000, "data": {"bleu_detok": 2.32342}}, {"step": 75000, "data": {"chrf_stalled": 11.0}}, {"step": 75000, "data": {"ce_mean_words_stalled": 12.0}}, {"step": 75000, "data": {"bleu_detok_stalled": 14.0}}, {"step": 76000, "data": {"epoch": 1}}, {"step": 76000, "data": {"sen": 58228467}}, {"step": 76000, "data": {"cost": 3.57263637}}, {"step": 76000, "data": {"time": 738.26}}, {"step": 76000, "data": {"rate": 23958.2}}, {"step": 76000, "data": {"gnorm": 0.7267}}, {"step": 77000, "data": {"epoch": 1}}, {"step": 77000, "data": {"sen": 58996742}}, {"step": 77000, "data": {"cost": 3.56980062}}, {"step": 77000, "data": {"time": 265.55}}, {"step": 77000, "data": {"rate": 67598.39}}, {"step": 77000, "data": {"gnorm": 0.6869}}, {"step": 78000, "data": {"epoch": 1}}, {"step": 78000, "data": {"sen": 59749542}}, {"step": 78000, "data": {"cost": 3.59234214}}, {"step": 78000, "data": {"time": 265.77}}, {"step": 78000, "data": {"rate": 67223.58}}, {"step": 78000, "data": {"gnorm": 0.688}}, {"step": 78000, "data": {"epoch": 1}}, {"step": 78000, "data": {"chrf": 24.802}}, {"step": 78000, "data": {"ce_mean_words": 3.24957}}, {"step": 78000, "data": {"bleu_detok": 2.23655}}, {"step": 78000, "data": {"chrf_stalled": 12.0}}, {"step": 78000, "data": {"ce_mean_words_stalled": 13.0}}, {"step": 78000, "data": {"bleu_detok_stalled": 15.0}}, {"step": 79000, "data": {"epoch": 1}}, {"step": 79000, "data": {"sen": 60530988}}, {"step": 79000, "data": {"cost": 3.54084754}}, {"step": 79000, "data": {"time": 681.19}}, {"step": 79000, "data": {"rate": 25986.9}}, {"step": 79000, "data": {"gnorm": 0.6737}}, {"step": 80000, "data": {"epoch": 1}}, {"step": 80000, "data": {"sen": 61289423}}, {"step": 80000, "data": {"cost": 3.58279347}}, {"step": 80000, "data": {"time": 265.15}}, {"step": 80000, "data": {"rate": 67361.77}}, {"step": 80000, "data": {"gnorm": 0.7041}}, {"step": 81000, "data": {"epoch": 1}}, {"step": 81000, "data": {"sen": 62063300}}, {"step": 81000, "data": {"cost": 3.53935003}}, {"step": 81000, "data": {"time": 323.5}}, {"step": 81000, "data": {"rate": 54605.04}}, {"step": 81000, "data": {"gnorm": 0.6877}}, {"step": 81000, "data": {"epoch": 1}}, {"step": 81000, "data": {"chrf": 24.7426}}, {"step": 81000, "data": {"ce_mean_words": 3.29356}}, {"step": 81000, "data": {"bleu_detok": 2.18849}}, {"step": 81000, "data": {"chrf_stalled": 13.0}}, {"step": 81000, "data": {"ce_mean_words_stalled": 14.0}}, {"step": 81000, "data": {"bleu_detok_stalled": 16.0}}, {"step": 82000, "data": {"epoch": 1}}, {"step": 82000, "data": {"sen": 62827190}}, {"step": 82000, "data": {"cost": 3.55813289}}, {"step": 82000, "data": {"time": 687.86}}, {"step": 82000, "data": {"rate": 25945.36}}, {"step": 82000, "data": {"gnorm": 0.8357}}, {"step": 83000, "data": {"epoch": 1}}, {"step": 83000, "data": {"sen": 63596233}}, {"step": 83000, "data": {"cost": 3.54824996}}, {"step": 83000, "data": {"time": 264.94}}, {"step": 83000, "data": {"rate": 67387.62}}, {"step": 83000, "data": {"gnorm": 0.7349}}, {"step": 84000, "data": {"epoch": 1}}, {"step": 84000, "data": {"sen": 64356161}}, {"step": 84000, "data": {"cost": 3.56180406}}, {"step": 84000, "data": {"time": 265.36}}, {"step": 84000, "data": {"rate": 66827.32}}, {"step": 84000, "data": {"gnorm": 0.6735}}, {"step": 84000, "data": {"epoch": 1}}, {"step": 84000, "data": {"chrf": 24.4751}}, {"step": 84000, "data": {"ce_mean_words": 3.32236}}, {"step": 84000, "data": {"bleu_detok": 2.15803}}, {"step": 84000, "data": {"chrf_stalled": 14.0}}, {"step": 84000, "data": {"ce_mean_words_stalled": 15.0}}, {"step": 84000, "data": {"bleu_detok_stalled": 17.0}}, {"step": 85000, "data": {"epoch": 1}}, {"step": 85000, "data": {"sen": 65119410}}, {"step": 85000, "data": {"cost": 3.54333282}}, {"step": 85000, "data": {"time": 687.61}}, {"step": 85000, "data": {"rate": 25685.99}}, {"step": 85000, "data": {"gnorm": 0.6645}}, {"step": 86000, "data": {"epoch": 1}}, {"step": 86000, "data": {"sen": 65897808}}, {"step": 86000, "data": {"cost": 3.54200935}}, {"step": 86000, "data": {"time": 324.42}}, {"step": 86000, "data": {"rate": 54820.86}}, {"step": 86000, "data": {"gnorm": 0.7072}}, {"step": 87000, "data": {"epoch": 1}}, {"step": 87000, "data": {"sen": 66651217}}, {"step": 87000, "data": {"cost": 3.54662633}}, {"step": 87000, "data": {"time": 263.54}}, {"step": 87000, "data": {"rate": 66658.1}}, {"step": 87000, "data": {"gnorm": 0.6941}}, {"step": 87000, "data": {"epoch": 1}}, {"step": 87000, "data": {"chrf": 24.3956}}, {"step": 87000, "data": {"ce_mean_words": 3.34832}}, {"step": 87000, "data": {"bleu_detok": 2.12922}}, {"step": 87000, "data": {"chrf_stalled": 15.0}}, {"step": 87000, "data": {"ce_mean_words_stalled": 16.0}}, {"step": 87000, "data": {"bleu_detok_stalled": 18.0}}, {"step": 88000, "data": {"epoch": 1}}, {"step": 88000, "data": {"sen": 67421577}}, {"step": 88000, "data": {"cost": 3.53749514}}, {"step": 88000, "data": {"time": 694.4}}, {"step": 88000, "data": {"rate": 26028.77}}, {"step": 88000, "data": {"gnorm": 0.7159}}, {"step": 89000, "data": {"epoch": 1}}, {"step": 89000, "data": {"sen": 68186767}}, {"step": 89000, "data": {"cost": 3.53344846}}, {"step": 89000, "data": {"time": 264.34}}, {"step": 89000, "data": {"rate": 66617.49}}, {"step": 89000, "data": {"gnorm": 0.7169}}, {"step": 90000, "data": {"epoch": 1}}, {"step": 90000, "data": {"sen": 68952783}}, {"step": 90000, "data": {"cost": 3.5275166}}, {"step": 90000, "data": {"time": 265.11}}, {"step": 90000, "data": {"rate": 67667.55}}, {"step": 90000, "data": {"gnorm": 0.6572}}, {"step": 90000, "data": {"epoch": 1}}, {"step": 90000, "data": {"chrf": 24.2047}}, {"step": 90000, "data": {"ce_mean_words": 3.37709}}, {"step": 90000, "data": {"bleu_detok": 2.12948}}, {"step": 90000, "data": {"chrf_stalled": 16.0}}, {"step": 90000, "data": {"ce_mean_words_stalled": 17.0}}, {"step": 90000, "data": {"bleu_detok_stalled": 19.0}}, {"step": 91000, "data": {"epoch": 1}}, {"step": 91000, "data": {"sen": 69726606}}, {"step": 91000, "data": {"cost": 3.51367259}}, {"step": 91000, "data": {"time": 753.74}}, {"step": 91000, "data": {"rate": 23548.03}}, {"step": 91000, "data": {"gnorm": 0.7368}}, {"step": 92000, "data": {"epoch": 1}}, {"step": 92000, "data": {"sen": 70509845}}, {"step": 92000, "data": {"cost": 3.53097773}}, {"step": 92000, "data": {"time": 266.69}}, {"step": 92000, "data": {"rate": 67224.17}}, {"step": 92000, "data": {"gnorm": 0.7282}}, {"step": 93000, "data": {"epoch": 1}}, {"step": 93000, "data": {"sen": 71251949}}, {"step": 93000, "data": {"cost": 3.52423358}}, {"step": 93000, "data": {"time": 264.3}}, {"step": 93000, "data": {"rate": 66839.29}}, {"step": 93000, "data": {"gnorm": 0.7065}}, {"step": 93000, "data": {"epoch": 1}}, {"step": 93000, "data": {"chrf": 23.9629}}, {"step": 93000, "data": {"ce_mean_words": 3.4132}}, {"step": 93000, "data": {"bleu_detok": 2.08293}}, {"step": 93000, "data": {"chrf_stalled": 17.0}}, {"step": 93000, "data": {"ce_mean_words_stalled": 18.0}}, {"step": 93000, "data": {"bleu_detok_stalled": 20.0}}, {"step": 94000, "data": {"epoch": 1}}, {"step": 94000, "data": {"sen": 72030379}}, {"step": 94000, "data": {"cost": 3.51761961}}, {"step": 94000, "data": {"time": 695.17}}, {"step": 94000, "data": {"rate": 25616.1}}, {"step": 94000, "data": {"gnorm": 0.7348}}, {"step": 95000, "data": {"epoch": 1}}, {"step": 95000, "data": {"sen": 72791500}}, {"step": 95000, "data": {"cost": 3.53031635}}, {"step": 95000, "data": {"time": 266.27}}, {"step": 95000, "data": {"rate": 67052.59}}, {"step": 95000, "data": {"gnorm": 0.764}}, {"step": 96000, "data": {"epoch": 1}}, {"step": 96000, "data": {"sen": 73550247}}, {"step": 96000, "data": {"cost": 3.51481533}}, {"step": 96000, "data": {"time": 325.42}}, {"step": 96000, "data": {"rate": 55178.5}}, {"step": 96000, "data": {"gnorm": 0.7224}}, {"step": 96000, "data": {"epoch": 1}}, {"step": 96000, "data": {"chrf": 23.7756}}, {"step": 96000, "data": {"ce_mean_words": 3.42841}}, {"step": 96000, "data": {"bleu_detok": 2.041}}, {"step": 96000, "data": {"chrf_stalled": 18.0}}, {"step": 96000, "data": {"ce_mean_words_stalled": 19.0}}, {"step": 96000, "data": {"bleu_detok_stalled": 21.0}}, {"step": 97000, "data": {"epoch": 1}}, {"step": 97000, "data": {"sen": 74324080}}, {"step": 97000, "data": {"cost": 3.50722432}}, {"step": 97000, "data": {"time": 694.86}}, {"step": 97000, "data": {"rate": 25362.68}}, {"step": 97000, "data": {"gnorm": 0.7574}}, {"step": 98000, "data": {"epoch": 1}}, {"step": 98000, "data": {"sen": 75086109}}, {"step": 98000, "data": {"cost": 3.50140643}}, {"step": 98000, "data": {"time": 264.8}}, {"step": 98000, "data": {"rate": 66748.63}}, {"step": 98000, "data": {"gnorm": 0.7289}}, {"step": 99000, "data": {"epoch": 1}}, {"step": 99000, "data": {"sen": 75855284}}, {"step": 99000, "data": {"cost": 3.50126433}}, {"step": 99000, "data": {"time": 265.42}}, {"step": 99000, "data": {"rate": 67311.11}}, {"step": 99000, "data": {"gnorm": 0.7395}}, {"step": 99000, "data": {"epoch": 1}}, {"step": 99000, "data": {"chrf": 23.7948}}, {"step": 99000, "data": {"ce_mean_words": 3.43583}}, {"step": 99000, "data": {"bleu_detok": 2.08156}}, {"step": 99000, "data": {"chrf_stalled": 19.0}}, {"step": 99000, "data": {"ce_mean_words_stalled": 20.0}}, {"step": 99000, "data": {"bleu_detok_stalled": 22.0}}, {"step": 100000, "data": {"epoch": 1}}, {"step": 100000, "data": {"sen": 76628927}}, {"step": 100000, "data": {"cost": 3.5060935}}, {"step": 100000, "data": {"time": 696.82}}, {"step": 100000, "data": {"rate": 25493.51}}, {"step": 100000, "data": {"gnorm": 0.7171}}, {"step": 101000, "data": {"epoch": 1}}, {"step": 101000, "data": {"sen": 77387085}}, {"step": 101000, "data": {"cost": 3.49618578}}, {"step": 101000, "data": {"time": 322.95}}, {"step": 101000, "data": {"rate": 54748.31}}, {"step": 101000, "data": {"gnorm": 0.8021}}, {"step": 102000, "data": {"epoch": 1}}, {"step": 102000, "data": {"sen": 78161035}}, {"step": 102000, "data": {"cost": 3.49717569}}, {"step": 102000, "data": {"time": 267.37}}, {"step": 102000, "data": {"rate": 67218.85}}, {"step": 102000, "data": {"gnorm": 0.7461}}, {"step": 102000, "data": {"epoch": 1}}, {"step": 102000, "data": {"chrf": 23.9278}}, {"step": 102000, "data": {"ce_mean_words": 3.44634}}, {"step": 102000, "data": {"bleu_detok": 2.08529}}, {"step": 102000, "data": {"chrf_stalled": 20.0}}, {"step": 102000, "data": {"ce_mean_words_stalled": 21.0}}, {"step": 102000, "data": {"bleu_detok_stalled": 23.0}}]
\ No newline at end of file
diff --git a/tracking/translations_parser/data.py b/tracking/translations_parser/data.py
index 44128b8fb..d79996f65 100644
--- a/tracking/translations_parser/data.py
+++ b/tracking/translations_parser/data.py
@@ -31,8 +31,9 @@ class TrainingEpoch:
     sen: int
     cost: float
     time: float
-    rate: float
+    rate: float  # Words per second
     gnorm: float
+    learning_rate: float | None = None  # Optional
 
 
 @dataclass
@@ -43,6 +44,11 @@ class ValidationEpoch:
     ce_mean_words: float
     bleu_detok: float
     perplexity: float = None  # optional
+    # Optional stalled validation metrics
+    chrf_stalled: int | None = None
+    ce_mean_words_stalled: int | None = None
+    bleu_detok_stalled: int | None = None
+    perplexity_stalled: float = None  # optional
 
 
 @dataclass
diff --git a/tracking/translations_parser/parser.py b/tracking/translations_parser/parser.py
index 8bd618581..1851634b4 100644
--- a/tracking/translations_parser/parser.py
+++ b/tracking/translations_parser/parser.py
@@ -16,7 +16,11 @@
 
 HEADER_RE = re.compile(r"(?<=\[)(?P<value>.+?)\] ")
 VALIDATION_RE = re.compile(
-    r"Ep\.[ :]+(?P<ep>\d+)[ :]+Up\.[ :]+(?P<up>\d+)[ :]+(?P<key>[\w-]+)[ :]+(?P<value>[\d\.]+)"
+    r"Ep\.[ :]+(?P<ep>\d+)"
+    r"[ :]+Up\.[ :]+(?P<up>\d+)"
+    r"[ :]+(?P<key>[\w-]+)"
+    r"[ :]+(?P<value>[\d\.]+)"
+    r"([ :]+stalled (?P<stalled>\d+) times)?"
 )
 TRAINING_RE = re.compile(
     r"Ep\.[ :]+(?P<epoch>\d+)[ :]+"
@@ -26,6 +30,7 @@
     r"Time[ :]+(?P<time>[\d\.]+)s[ :]+"
     r"(?P<rate>[\d\.]+) words\/s[ :]+"
     r"gNorm[ :]+(?P<gnorm>[\d\.]+)"
+    r"([ :]+L.r. (?P<learning_rate>[\d\.e-]+))?"
 )
 
 # Expected version of Marian for a clean parsing
@@ -105,13 +110,18 @@ def parse_training_log(self, text: str) -> TrainingEpoch | None:
         match = TRAINING_RE.match(text)
         if not match:
             return None
-        values = match.groupdict()
-        # Update sen value from 1,234,567 to 1_234_567 that Python interprets
+        # Filter out null values
+        values = {k: v for k, v in match.groupdict().items() if v is not None}
+        # Update sen from 1,234,567 to 1_234_567 that Python can interpret
         values["sen"] = values["sen"].replace(",", "_")
-        # Transform values to match output types
-        training_epoch = TrainingEpoch(
-            **{k: TrainingEpoch.__annotations__[k](v) for k, v in values.items()}
-        )
+        # Cast values to match output types
+        casted_values = {
+            k: TrainingEpoch.__annotations__[k](v)
+            if callable(TrainingEpoch.__annotations__[k])
+            else float(v)
+            for k, v in values.items()
+        }
+        training_epoch = TrainingEpoch(**casted_values)
         self.training.append(training_epoch)
         for publisher in self.publishers:
             try:
@@ -128,16 +138,25 @@ def parse_validation_log(
         """Parses a validation entry on multiple lines."""
         if ("valid",) not in headers or not (match := VALIDATION_RE.match(text)):
             return None
-        epoch, up, key, val = match.groups()
+        results = match.groupdict()
         # Replace items keys to match ValidationEpoch dataclass
-        key = key.replace("-", "_")
-        # Transform values to match output types
-        epoch, up = int(epoch), int(up)
-        val = ValidationEpoch.__annotations__[key](val)
+        key = results["key"].replace("-", "_")
+        epoch, up = int(results["ep"]), int(results["up"])
         entry = self._validation_entries[(epoch, up)]
-        entry[key] = val
+        # Transform values to match output types
+        entry[key] = ValidationEpoch.__annotations__[key](results["value"])
+        if results["stalled"] is not None:
+            entry[f"{key}_stalled"] = float(results["stalled"])
         # Build a validation epoch from multiple lines
-        expected_keys = set(ValidationEpoch.__annotations__.keys()) - {"epoch", "up", "perplexity"}
+        expected_keys = set(
+            key
+            for key in ValidationEpoch.__annotations__.keys()
+            if not (
+                # Stalled data are not necessary present on validation entries
+                key.endswith("_stalled")
+                or key in ("epoch", "up", "perplexity")
+            )
+        )
         if not (expected_keys - set(entry.keys())):
             validation_epoch = ValidationEpoch(epoch=epoch, up=up, **entry)
             self.validation.append(validation_epoch)