feat: changed `<reasoning>` tags to `<think>

rl_helpers.py

@@ -68,7 +68,7 @@ def build_user_prompt(q):
 Given a question, answer it using by doing searches using the search_corpus tool.
 To use the search_corpus tool, respond with a JSON for a function call with its proper arguments.
 
-You may also reason in any message, thinking step by step about how to answer the question. Wrap your reasoning in <reasoning> and </reasoning> tags.
+You may also reason in any message, think step by step about how to answer the question. Wrap your reasoning in <think> and </think> tags.
 
 {json.dumps(SEARCH_TOOL_DEFINITION, indent=2)}
 
@@ -140,17 +140,17 @@ def extract_json_objects(text):
 
 def remove_reasoning(text: str) -> str:
     """
-    Removes all content between <reasoning> and </reasoning> tags,
+    Removes all content between <think> and </think> tags,
     including the tags themselves.
 
     Parameters:
-        text (str): The input text that may contain <reasoning>...</reasoning> tags.
+        text (str): The input text that may contain <think>...</think> tags.
 
     Returns:
         str: The text with the tags and their content removed.
     """
-    # The regex pattern matches from <reasoning> to </reasoning> non-greedily.
+    # The regex pattern matches from <think> to </think> non-greedily.
-    pattern = r"<reasoning>.*?</reasoning>"
+    pattern = r"<think>.*?</think>"
     cleaned_text = re.sub(pattern, "", text, flags=re.DOTALL)
     return cleaned_text
 
@@ -495,6 +495,7 @@ def check_student_answers(
     return results
 
 
+# Reward Functions
 def build_reward_correctness_fn(generate_fn, tokenizer):
     def reward_correctness(prompts, completions, **reward_kwargs):
         teacher_answers = reward_kwargs["answer"]
@@ -525,6 +526,14 @@ def reward_formatting(prompts, completions, **reward_kwargs):
     return [0.7 if not e else 0 for e in has_error]
 
 
+# def reward_retry_behavior(prompts, completions, **reward_kwargs):
+#     pass
+
+
+# def reward_exact_match_chunk_query(prompts, completions, **reward_kwargs):
+#     pass
+
+
 def run_eval(generate_fn, verify_fn, tokenizer):
     train_dataset, test_dataset = get_qa_dataset()
     questions = test_dataset["prompt"]