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# RLAIF 假设
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assumption = {
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    "ai_can_judge": "强大的 LLM（如 GPT-4）可以评判偏好",
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    "distillation_works": "通过蒸馏，模型可以学习 AI 偏好",
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    "quality_parity": "AI 反馈可以达到人类反馈的质量"
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}

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class AIFeedbackGenerator:
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    """使用 LLM 生成偏好反馈"""
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    def __init__(self, judge_model):
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        self.judge = judge_model
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    def generate_preference(self, prompt, response_a, response_b):
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        """
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        让 LLM 判断两个回答的偏好
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        """
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        evaluation_prompt = f"""
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        请判断以下两个回答哪个更好。
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        提示：{prompt}
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        回答 A：{response_a}
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        回答 B：{response_b}
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        分析两个回答的优缺点，然后给出最终判断。
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        哪个回答更好？为什么？
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        """
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        judgment = self.judge.generate(evaluation_prompt)
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        # 解析偏好
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        if "A 更好" in judgment:
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            preference = {"preferred": "A", "confidence": self.compute_confidence(judgment)}
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        elif "B 更好" in judgment:
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            preference = {"preferred": "B", "confidence": self.compute_confidence(judgment)}
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        else:
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            preference = {"preferred": "tie", "confidence": 0}
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        return preference

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# RLAIF 使用的评判提示
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JUDGE_TEMPLATE = """
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你是一个专业的 AI 助手评估员。
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给定一个提示和两个候选回答，你需要判断哪个回答更好。
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评分标准：
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1. 帮助性：回答是否有用
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2. 诚实性：回答是否准确
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3. 无害性：回答是否有害
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请先分析两个回答的优缺点，然后给出最终判断。
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格式：
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分析：[你的分析]
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判断：[A/B/平局]
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置信度：[高/中/低]
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"""
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def create_judge_prompt(prompt, response_a, response_b):
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    return f"""
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    {JUDGE_TEMPLATE}
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    提示：{prompt}
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    回答 A：{response_a}
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    回答 B：{response_b}
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    """

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class DistilledRLAIF:
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    """
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    蒸馏 RLAIF：先训练奖励模型，再用 RLHF
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    """
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    def __init__(self, policy_model, reward_model):
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        self.policy = policy_model
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        self.reward = reward_model
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    def train_reward_model(self, demonstrations):
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        """
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        用 AI 偏好训练奖励模型
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        """
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        for prompt, response_a, response_b in demonstrations:
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            # 获取 AI 偏好
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            ai_pref = self.get_ai_preference(prompt, response_a, response_b)
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            # 训练奖励模型
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            if ai_pref["preferred"] == "A":
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                reward_a = 1
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                reward_b = 0
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            else:
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                reward_a = 0
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                reward_b = 1
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            self.reward.update(prompt, response_a, response_b, reward_a, reward_b)
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    def rl_optimize(self):
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        """
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        用奖励模型进行 PPO 优化
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        """
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        # 与标准 RLHF 相同
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        pass

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class DirectRLAIF:
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    """
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    直接 RLAIF：直接用 AI 反馈优化策略
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    """
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    def __init__(self, policy_model, judge_model):
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        self.policy = policy_model
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        self.judge = judge_model
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    def generate_and_score(self, prompt):
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        """
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        生成回答并用 AI 评分
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        """
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        # 生成多个候选回答
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        responses = self.policy.sample(prompt, n=4)
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        # 两两比较
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        pairwise_scores = []
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        for i in range(len(responses)):
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            for j in range(i+1, len(responses)):
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                pref = self.judge.generate_preference(
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                    prompt, responses[i], responses[j]
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                )
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                pairwise_scores.append((i, j, pref))
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        # 汇总评分
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        scores = self.aggregate_scores(responses, pairwise_scores)
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        # 直接用这些评分优化策略
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        self.policy.update_with_scores(responses, scores)

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# AI 反馈 vs 人类反馈对比
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quality_comparison = {
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    "agreement": {
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        "AI_labeler_vs_human": "87%",  # AI 与人类标注的一致性
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        "human_vs_human": "73%"        # 人类之间的标注一致性
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    },
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    # AI 标注者之间更一致！
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}

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class PositionBiasHandler:
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    """处理回答位置偏差"""
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    def __init__(self):
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        self.bias_corrections = []
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    def balanced_presentation(self, response_a, response_b):
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        """
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        平衡呈现两个回答，避免位置偏差
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        """
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        # 50% 的概率交换位置
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        if random.random() < 0.5:
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            return response_b, response_a
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        return response_a, response_b
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    def counterfactual_augmentation(self, prompt, response_a, response_b):
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        """
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        反事实增强：交换位置后重新标注
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        """
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        # 位置 1
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        pref1 = self.label(prompt, response_a, response_b)
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        # 位置交换
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        pref2 = self.label(prompt, response_b, response_a)
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        # 合并：取一致的结果，或置信度更高的
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        return self.merge_preferences(pref1, pref2)

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class ConfidenceCalibration:
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    """置信度校准"""
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    def __init__(self):
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        self.confidence_bins = {}
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    def calibrate(self, claimed_confidence, actual_accuracy):
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        """
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        校准置信度
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        """
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        bin_id = self.get_bin(claimed_confidence)
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        calibrated = self.actual_accuracy[bin_id]
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        # 调整置信度使其更准确
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        return calibrated

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# RLAIF 的潜在问题
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problems = {
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    "sycophancy": "AI 可能过度恭维人类的喜好",
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    "bias_amplification": "AI 偏好可能放大现有偏见",
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    "closed_loop": "用 AI 反馈训练的模型可能越来越像 AI"
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}