diff --git a/metaclaw/data_formatter.py b/metaclaw/data_formatter.py
index 13eae0c..908969b 100644
--- a/metaclaw/data_formatter.py
+++ b/metaclaw/data_formatter.py
@@ -219,6 +219,12 @@ def compute_advantages(batch: list[ConversationSample]) -> list[float]:
     Centre-and-scale rewards within the batch (GRPO style: (r - mean) / (std + eps)).
 
     Returns a list of float advantages, one per sample.
+
+    When the batch contains only one sample (or all rewards are identical),
+    std is 0 and the normalised advantage collapses to 0 for every sample,
+    producing no gradient signal at all.  In that case we fall back to
+    returning the raw (centred) rewards so the training step still gets a
+    meaningful update.
     """
     if not batch:
         return []
@@ -226,5 +232,6 @@ def compute_advantages(batch: list[ConversationSample]) -> list[float]:
     mean_r = sum(rewards) / len(rewards)
     variance = sum((r - mean_r) ** 2 for r in rewards) / len(rewards)
     std_r = variance ** 0.5
-    eps = 1e-8
-    return [(r - mean_r) / (std_r + eps) for r in rewards]
+    if std_r < 1e-8:
+        return [r - mean_r if len(batch) > 1 else r for r in rewards]
+    return [(r - mean_r) / std_r for r in rewards]
diff --git a/metaclaw/prm_scorer.py b/metaclaw/prm_scorer.py
index cab01c6..abb737d 100644
--- a/metaclaw/prm_scorer.py
+++ b/metaclaw/prm_scorer.py
@@ -167,6 +167,7 @@ def __init__(
         temperature: float = 0.6,
         max_new_tokens: int = 1024,
         llm_client=None,
+        timeout: float = 120.0,
     ):
         if llm_client is not None:
             self._client = llm_client
@@ -180,6 +181,7 @@ def __init__(
         self.prm_m = prm_m
         self.temperature = temperature
         self.max_new_tokens = max_new_tokens
+        self.timeout = timeout
 
     async def evaluate(
         self,
@@ -225,15 +227,21 @@ async def _query_once(
         self, messages: list[dict], vote_id: int
     ) -> tuple[Optional[int], str]:
         try:
-            completion = await asyncio.to_thread(
-                self._client.chat.completions.create,
-                model=self.prm_model,
-                messages=messages,
-                temperature=self.temperature,
-                max_completion_tokens=self.max_new_tokens,
+            completion = await asyncio.wait_for(
+                asyncio.to_thread(
+                    self._client.chat.completions.create,
+                    model=self.prm_model,
+                    messages=messages,
+                    temperature=self.temperature,
+                    max_completion_tokens=self.max_new_tokens,
+                ),
+                timeout=self.timeout,
             )
             content = completion.choices[0].message.content or ""
             return _parse_prm_score(content), content
+        except asyncio.TimeoutError:
+            logger.warning("[PRMScorer] query timed out after %.0fs (vote %d)", self.timeout, vote_id)
+            return None, ""
         except Exception as e:
             logger.warning("[PRMScorer] query failed (vote %d): %s", vote_id, e)
             return None, ""
diff --git a/metaclaw/skill_evolver.py b/metaclaw/skill_evolver.py
index 2c073b0..dd98736 100644
--- a/metaclaw/skill_evolver.py
+++ b/metaclaw/skill_evolver.py
@@ -82,7 +82,7 @@ def __init__(
             self._openai_client = None
         else:
             self._custom_client = None
-            api_key = os.environ.get("OPENAI_API_KEY", "aB7cD9eF2gH5iJ8kL1mN4oP6qR3sT0uV")
+            api_key = os.environ.get("OPENAI_API_KEY", "")
             base_url = os.environ.get(
                 "OPENAI_BASE_URL",
                 "https://openai-api.shenmishajing.workers.dev/v1",