初始化项目，由ModelHub XC社区提供模型

Model: ayh015/myLightningOPD
Source: Original Platform

slime/rollout/rm_hub/gpqa.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import re
+import string
+from collections.abc import Iterable
+
+DEFAULT_VALID_LETTERS = list(string.ascii_uppercase[:8])
+
+
+def _strip_chain_of_thought(text: str) -> str:
+    if not text:
+        return ""
+
+    if "</think>" in text:
+        return text.rsplit("</think>", 1)[-1]
+
+    return text
+
+
+def _normalize_text(text: str) -> str:
+    return re.sub(r"[^a-z0-9]+", " ", text.lower()).strip()
+
+
+def _extract_letter_from_response(response: str, valid_letters: Iterable[str]) -> str | None:
+    """
+    Best-effort extraction of the selected option letter from the model response.
+    """
+    if not response:
+        return None
+
+    text = _strip_chain_of_thought(response)
+    patterns = [
+        r"(?:answer|option|choice)\s*(?:is|:)?\s*([A-Z])",
+        r"([A-Z])\s*(?:is\s*(?:the)?\s*correct)",
+        r"final\s*(?:answer|option)\s*(?:is|:)?\s*([A-Z])",
+    ]
+
+    valid_letters = {letter.upper() for letter in valid_letters}
+    for pattern in patterns:
+        match = re.search(pattern, text, flags=re.IGNORECASE)
+        if match:
+            letter = match.group(1).upper()
+            if letter in valid_letters:
+                return letter
+
+    # Fallback: last standalone capital letter that is valid.
+    candidates = re.findall(r"\b([A-Z])\b", text)
+    for letter in reversed(candidates):
+        letter = letter.upper()
+        if letter in valid_letters:
+            return letter
+
+    return None
+
+
+def compute_gpqa_reward(response: str, label, metadata: dict | None = None) -> float:
+    """Rule-based scorer for GPQA-style multiple-choice evaluation."""
+    if response is None:
+        return 0.0
+
+    metadata = metadata or {}
+
+    choices = metadata.get("choices")
+    if isinstance(choices, dict):
+        choices = list(choices.values())
+    elif choices is not None:
+        choices = list(choices)
+
+    valid_letters = metadata.get("valid_letters")
+    if valid_letters:
+        valid_letters = [str(letter).upper() for letter in valid_letters]
+    elif choices:
+        valid_letters = list(string.ascii_uppercase[: len(choices)])
+    else:
+        valid_letters = DEFAULT_VALID_LETTERS
+
+    correct_letter = metadata.get("correct_letter")
+    if isinstance(correct_letter, str):
+        correct_letter = correct_letter.strip().upper()
+    else:
+        correct_letter = None
+
+    label_text = None
+    if isinstance(label, str):
+        label_text = label.strip()
+        if len(label_text) == 1 and label_text.upper() in valid_letters and not correct_letter:
+            correct_letter = label_text.upper()
+    elif isinstance(label, (int, float)):
+        idx = int(label)
+        if 0 <= idx < len(valid_letters):
+            correct_letter = valid_letters[idx]
+
+    if not correct_letter and choices and label_text:
+        normalized_label = _normalize_text(label_text)
+        for idx, choice in enumerate(choices):
+            if _normalize_text(str(choice)) == normalized_label:
+                correct_letter = valid_letters[idx]
+                metadata.setdefault("correct_answer", choice)
+                break
+
+    extracted_letter = _extract_letter_from_response(response, valid_letters)
+    if extracted_letter and correct_letter:
+        return 1.0 if extracted_letter == correct_letter else 0.0
+
+    candidate_answers = []
+    if correct_letter and choices:
+        try:
+            idx = valid_letters.index(correct_letter)
+        except ValueError:
+            idx = None
+        if idx is not None and idx < len(choices):
+            candidate_answers.append(str(choices[idx]))
+
+    for key in ("correct_answer", "answer_text"):
+        value = metadata.get(key)
+        if value:
+            candidate_answers.append(str(value))
+
+    if label_text:
+        candidate_answers.append(label_text)
+
+    normalized_targets = {_normalize_text(text) for text in candidate_answers if text}
+    normalized_response = _normalize_text(_strip_chain_of_thought(response))
+    for target in normalized_targets:
+        if target and target in normalized_response:
+            return 1.0
+
+    if extracted_letter and not correct_letter and label_text:
+        return 1.0 if extracted_letter == label_text.strip().upper() else 0.0
+
+    return 0.0