diff --git a/examples/summarize_daily_cnn/t5_summarize_daily_cnn.py b/examples/summarize_daily_cnn/t5_summarize_daily_cnn.py
index 4c3a56758..97f5633fe 100755
--- a/examples/summarize_daily_cnn/t5_summarize_daily_cnn.py
+++ b/examples/summarize_daily_cnn/t5_summarize_daily_cnn.py
@@ -1,8 +1,6 @@
 from typing import List
 
 from datasets import load_dataset
-from tqdm import tqdm
-from transformers import AutoTokenizer
 
 import trlx
 from trlx.data.configs import (
@@ -93,11 +91,10 @@
 
 if __name__ == "__main__":
 
-    def reward_fn(samples: List[str], prompts: List[str], outputs: List[str]):
-        original_summaries = [prompt_label[prompt.strip()] for prompt in prompts]
+    def reward_fn(samples: List[str], prompts: List[str], outputs: List[str], original_summaries: List[str], **kwargs):
         scores = [
-            meteor.compute(predictions=[output.strip()], references=[original])["meteor"]
-            for (original, output) in zip(original_summaries, outputs)
+            meteor.compute(predictions=[output.strip()], references=[original_summary])["meteor"]
+            for (original_summary, output) in zip(original_summaries, outputs)
         ]
         return scores
 
@@ -112,31 +109,11 @@ def reward_fn(samples: List[str], prompts: List[str], outputs: List[str]):
     val_prompts = ["Summarize: " + prompt for prompt in dataset["validation"]["article"][0:1000]]
     val_summaries = dataset["validation"]["highlights"][0:1000]
 
-    # make dictionary of prompts and labels to use for reward function
-    tokenizer = AutoTokenizer.from_pretrained(config.model.model_path)
-    tokenizer.padding_side = "left"
-    tokenizer.truncation_side = "right"
-    tokenizer.sep_token = "<sep>"
-    prompt_label = {}
-    max_length = config.train.seq_length - config.method.gen_kwargs["max_new_tokens"]
-
-    for i in tqdm(range(len(prompts))):
-        key = tokenizer.decode(
-            tokenizer(prompts[i], truncation=True, max_length=max_length, add_special_tokens=False)["input_ids"],
-            skip_special_tokens=True,
-        )  # get prompt like trlx's prompt
-        prompt_label[key.strip()] = summaries[i]
-
-    for i in tqdm(range(len(val_prompts))):
-        key = tokenizer.decode(
-            tokenizer(val_prompts[i], truncation=True, max_length=max_length, add_special_tokens=False)["input_ids"],
-            skip_special_tokens=True,
-        )  # get prompt like trlx's prompt
-        prompt_label[key.strip()] = val_summaries[i]
-
     trlx.train(
         reward_fn=reward_fn,
-        prompts=prompts,
-        eval_prompts=val_prompts,
+        prompts=[{"prompt": prompt, "original_summaries": summary} for prompt, summary in zip(prompts, summaries)],
+        eval_prompts=[
+            {"prompt": prompt, "original_summaries": summary} for prompt, summary in zip(val_prompts, val_summaries)
+        ],
         config=config,
     )