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# Superalignment 的核心问题
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problem_analogy = {
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    "weak_supervisor": "GPT-2 级别的模型",
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    "strong_student": "GPT-4 级别的模型",
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    "task": "NLP 任务、西洋棋、奖励建模",
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    "question": "弱监督能唤醒强模型的全部能力吗？"
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}

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class WeakToStrongExperiment:
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    """弱到强泛化实验"""
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    def __init__(self):
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        self.weak_models = ["gpt2-small", "gpt2-medium"]
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        self.strong_models = ["gpt4"]
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    def run_experiment(self, task):
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        """
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        用弱模型监督强模型
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        """
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        results = {}
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        for weak in self.weak_models:
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            for strong in self.strong_models:
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                # 用 weak 模型生成监督信号
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                supervision = self.generate_supervision(weak, task)
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                # 用 supervision 微调 strong 模型
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                fine_tuned = self.finetune(strong, supervision)
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                # 评估结果
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                accuracy = self.evaluate(fine_tuned, task)
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                results[f"{weak}_on_{strong}"] = accuracy
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        return results

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class ConfidenceLoss:
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    """
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    辅助置信损失：帮助强模型区分正确和错误的监督
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    """
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    def __init__(self, lambda_conf=0.1):
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        self.lambda_conf = lambda_conf
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    def compute_loss(self, strong_output, weak_labels, strong_logits):
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        """
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        总损失 = 标准交叉熵 + 辅助置信损失
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        """
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        # 标准损失：学习弱监督
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        ce_loss = F.cross_entropy(strong_logits, weak_labels)
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        # 辅助置信损失：鼓励模型对自己有信心
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        # 当模型对某个预测很自信时，给予奖励
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        conf_loss = self.auxiliary_confidence_loss(strong_logits, weak_labels)
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        return ce_loss + self.lambda_conf * conf_loss
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    def auxiliary_confidence_loss(self, logits, labels):
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        """
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        辅助置信损失
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        """
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        # 计算每个样本的置信度
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        probs = F.softmax(logits, dim=-1)
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        target_probs = probs.gather(1, labels.unsqueeze(1))
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        # 鼓励高置信度
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        return -target_probs.mean()

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class WeakToStrongWithConfidence(nn.Module):
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    """带辅助置信损失的弱到强泛化"""
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    def __init__(self, strong_model):
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        super().__init__()
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        self.model = strong_model
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        self.aux_head = nn.Linear(hidden_size, 1)  # 置信度预测头
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    def forward(self, x, weak_labels):
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        logits = self.model(x)
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        # 置信度
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        confidence = torch.sigmoid(self.aux_head(logits))
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        # 置信度加权的交叉熵
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        weights = confidence.squeeze()
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        loss = F.cross_entropy(logits, weak_labels, reduction='none')
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        weighted_loss = (loss * weights).mean()
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        return weighted_loss

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# 不同领域的弱到强泛化结果
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results = {
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    "nlp": {
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        "weak_supervisor": 60,
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        "simple_finetune": 55,
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        "with_confidence": 72
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    },
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    "chess": {
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        "weak_supervisor": 50,
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        "simple_finetune": 52,
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        "with_confidence": 68
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    },
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    "reward_modeling": {
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        "weak_supervisor": 65,
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        "simple_finetune": 60,
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        "with_confidence": 75
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    }
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}

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# 辅助置信损失帮助强模型识别错误监督
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class MechanismAnalysis:
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    """
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    辅助置信损失的工作机制
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    """
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    def how_it_works(self):
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        """
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        1. 强模型在看到弱监督信号时，对某些样本更有信心
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        2. 置信度头学习识别：
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           - 弱监督正确的样本（高置信度）
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           - 弱监督可能错误的样本（低置信度）
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        3. 置信度加权的损失：
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           - 对有信心的预测更重视
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           - 对没信心的预测降低权重
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        4. 结果：
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           - 强模型学会部分忽略弱监督的错误
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           - 保持对自己正确判断的信心
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        """
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        pass

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# 未来研究方向
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future_directions = {
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    "better_losses": "更有效的辅助损失函数",
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    "iterated_superposition": "迭代弱到强泛化",
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    "theoretical_foundation": "理论解释和分析",
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    "scalable_oversight": "可扩展的监督方法"
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}