diff --git a/environments/fundamental_prediction_environment.py b/environments/fundamental_prediction_environment.py
index a38c561c..4b283b03 100644
--- a/environments/fundamental_prediction_environment.py
+++ b/environments/fundamental_prediction_environment.py
@@ -4,11 +4,13 @@ from typing import List, Optional, Tuple, Union, Dict
 
 from datasets import load_dataset
 from tqdm.asyncio import tqdm_asyncio
+import wandb
 
 from atroposlib.envs.base import (
     BaseEnv,
     BaseEnvConfig,
     EvalHandlingEnum,
+    Item,
     OpenaiConfig,
     ScoredDataGroup,
 )
@@ -536,6 +538,64 @@ class FundamentalPredictionEnv(BaseEnv):
 
         await super().wandb_log(wandb_metrics)
 
+    async def add_rollouts_for_wandb(
+        self,
+        scored_data: Union[ScoredDataGroup, List[ScoredDataGroup]],
+        item: Item = None,
+    ):
+        # Initialize rollouts_for_wandb if not exists
+        if not hasattr(self, "rollouts_for_wandb"):
+            self.rollouts_for_wandb = []
+
+        # Get number of examples to keep
+        num_keep = getattr(self.config, "num_rollouts_per_group_for_logging", -1)
+        
+        if num_keep == -1:
+            num_keep = self.config.group_size
+
+        # Get fundamental metric from item
+        fundamental_metric = item[3]
+
+        # Add examples to rollouts
+        self.rollouts_for_wandb.append(
+            [
+                (
+                    self.tokenizer.decode(scored_data["tokens"][i]),
+                    scored_data["scores"][i],
+                    item[1],  # expected direction (maintained/raised/reduced)
+                    item[2],  # expected magnitude
+                    fundamental_metric,  # metric type being predicted
+                )
+                for i in range(min(num_keep, len(scored_data["tokens"])))
+            ]
+        )
+
+        # Keep buffer size limited
+        max_rollouts = getattr(self.config, "num_rollouts_to_keep", 10)
+        if len(self.rollouts_for_wandb) > max_rollouts:
+            self.rollouts_for_wandb.pop(0)
+
+    async def create_rollout_table(self, wandb_metrics):
+        if hasattr(self, "rollouts_for_wandb") and len(self.rollouts_for_wandb) > 0:
+            table = wandb.Table(columns=[
+                "text", 
+                "score", 
+                "expected_direction", 
+                "expected_magnitude",
+                "fundamental_metric"
+            ])
+            
+            for group in self.rollouts_for_wandb:
+                for item in group:
+                    table.add_data(item[0], item[1], item[2], item[3], item[4])
+                    
+            wandb_metrics["train/rollouts"] = table
+            
+        # Clear rollouts after logging
+        self.rollouts_for_wandb = []
+        
+        return wandb_metrics
+
 
 if __name__ == "__main__":
     FundamentalPredictionEnv.cli()