Add zero_std all_zero_ratio and all_one_ratio metrics

miles/ray/rollout.py

@@ -1281,7 +1281,18 @@ def _is_zero_std(samples: list[Sample]):
 
     interesting_rewards = [str(round(g[0].get_reward_value(args), 1)) for g in interesting_sample_groups]
 
-    return {f"zero_std/count_{reward}": len(items) for reward, items in group_by(interesting_rewards).items()}
+    counts = {reward: len(items) for reward, items in group_by(interesting_rewards).items()}
+    log_dict = {f"zero_std/count_{reward}": count for reward, count in counts.items()}
+
+    # Percentages over total groups, so "too hard" (all-0) and "too easy"
+    # (all-1) rates are comparable across runs without needing to know the
+    # rollout batch size.
+    total_groups = len(all_sample_groups)
+    if total_groups > 0:
+        log_dict["zero_std/all_zero_percentage"] = counts.get("0.0", 0) / total_groups
+        log_dict["zero_std/all_one_percentage"] = counts.get("1.0", 0) / total_groups
+
+    return log_dict
 
 
 def _compute_spec_metrics(args, all_samples: list[Sample]):