大模型蒸馏技术 2026：从 GPT-5.5 到 7B 模型的能力迁移

知识蒸馏
├── 响应蒸馏 (Response Distillation)
│   ├── SFT 蒸馏（最常用）
│   ├── DPO 蒸馏
│   └── Best-of-N 蒸馏
├── 特征蒸馏 (Feature Distillation)
│   ├── Logit 蒸馏
│   ├── 中间层蒸馏
│   └── Attention 蒸馏
├── Agent 蒸馏 (Agent Distillation)
│   ├── 工具使用蒸馏
│   ├── 推理链蒸馏
│   └── 规划能力蒸馏
└── 数据蒸馏 (Data Distillation)
    ├── 合成数据生成
    ├── 数据增强
    └── 自指令

class SFTDistillation:
    def __init__(self, teacher_model, student_model):
        self.teacher = teacher_model  # GPT-5.5 API
        self.student = student_model  # 本地 7B 模型
    
    def generate_training_data(self, prompts: list):
        """用 Teacher 生成高质量训练数据"""
        training_data = []
        
        for prompt in prompts:
            # 1. Teacher 生成高质量回复
            teacher_response = self.teacher.generate(
                prompt,
                temperature=0.3,  # 低温度保证质量稳定
                max_tokens=2048
            )
            
            # 2. 质量过滤
            quality_score = self._assess_quality(prompt, teacher_response)
            if quality_score > 0.7:
                training_data.append({
                    "prompt": prompt,
                    "response": teacher_response,
                    "quality_score": quality_score
                })
        
        return training_data
    
    def distill(self, training_data: list, output_dir: str):
        """SFT 训练 Student 模型"""
        # 构建 SFT 数据集
        dataset = self._build_dataset(training_data)
        
        # SFT 训练
        trainer = SFTTrainer(
            model=self.student,
            train_dataset=dataset,
            args=TrainingArguments(
                output_dir=output_dir,
                num_train_epochs=3,
                per_device_train_batch_size=4,
                learning_rate=2e-5,
                lr_scheduler_type="cosine",
                warmup_ratio=0.05,
                bf16=True,
            )
        )
        
        trainer.train()
        return trainer.model

class CoTDistillation:
    def __init__(self, teacher, student):
        self.teacher = teacher
        self.student = student
    
    def generate_cot_data(self, problems: list):
        """生成带推理链的训练数据"""
        data = []
        
        for problem in problems:
            # Teacher 生成详细推理过程
            cot_prompt = f"""
请一步步解决以下问题，展示完整的推理过程：

问题：{problem}

请用以下格式回答：
<reasoning>
[详细推理过程]
</reasoning>
<answer>
[最终答案]
</answer>
"""
            teacher_response = self.teacher.generate(cot_prompt)
            
            # 验证答案正确性
            answer = self._extract_answer(teacher_response)
            if self._verify_answer(problem, answer):
                data.append({
                    "prompt": problem,
                    "response": teacher_response,
                    "answer": answer
                })
        
        return data

class AgentDistillation:
    """蒸馏 Agent 的工具使用和规划能力"""
    
    def generate_agent_data(self, tasks: list):
        data = []
        
        for task in tasks:
            # 1. Teacher Agent 执行任务
            trajectory = self.teacher.execute_task(task)
            # trajectory 包含：思考过程、工具调用、工具返回、最终答案
            
            # 2. 转为训练数据
            for step in trajectory:
                data.append({
                    "messages": [
                        {"role": "system", "content": AGENT_SYSTEM_PROMPT},
                        {"role": "user", "content": step["context"]},
                        {"role": "assistant", "content": step["thought"]},
                        {"role": "tool", "content": step["tool_result"]},
                        {"role": "assistant", "content": step["next_action"]}
                    ]
                })
        
        return data
    
    def evaluate_agent_capability(self, model, test_tasks: list):
        """评估 Student 的 Agent 能力"""
        results = {
            "tool_selection_accuracy": 0,
            "task_completion_rate": 0,
            "avg_steps": 0,
            "reasoning_quality": 0
        }
        
        for task in test_tasks:
            trajectory = model.execute_task(task)
            
            # 评估工具选择是否正确
            correct_tools = self._compare_tool_usage(trajectory, task)
            results["tool_selection_accuracy"] += correct_tools
            
            # 评估任务完成
            if trajectory[-1]["success"]:
                results["task_completion_rate"] += 1
            
            results["avg_steps"] += len(trajectory)
        
        # 归一化
        n = len(test_tasks)
        results = {k: v / n for k, v in results.items()}
        return results

class LogitDistillation:
    """将 Teacher 的输出分布迁移到 Student"""
    
    def __init__(self, teacher, student, temperature=2.0, alpha=0.5):
        self.teacher = teacher
        self.student = student
        self.temperature = temperature
        self.alpha = alpha  # distillation loss weight
    
    def compute_loss(self, input_ids, labels):
        # 1. Teacher logits（不计算梯度）
        with torch.no_grad():
            teacher_outputs = self.teacher(input_ids)
            teacher_logits = teacher_outputs.logits / self.temperature
            teacher_probs = F.softmax(teacher_logits, dim=-1)
        
        # 2. Student logits
        student_outputs = self.student(input_ids)
        student_logits = student_outputs.logits / self.temperature
        
        # 3. Distillation Loss (KL Divergence)
        distill_loss = F.kl_div(
            F.log_softmax(student_logits, dim=-1),
            teacher_probs,
            reduction="batchmean"
        ) * (self.temperature ** 2)
        
        # 4. Task Loss (Cross Entropy)
        task_loss = F.cross_entropy(
            student_outputs.logits.view(-1, student_outputs.logits.size(-1)),
            labels.view(-1)
        )
        
        # 5. Combined Loss
        total_loss = self.alpha * distill_loss + (1 - self.alpha) * task_loss
        return total_loss

class DistillationDataStrategy:
    def generate_diverse_data(self, seed_prompts: list, target_count: int):
        """生成多样化的蒸馏数据"""
        data = []
        
        # 1. 从种子 prompt 扩展
        expanded_prompts = self._expand_prompts(seed_prompts, multiplier=5)
        
        # 2. 多温度采样（增加多样性）
        for temp in [0.3, 0.5, 0.7, 0.9]:
            for prompt in expanded_prompts:
                response = self.teacher.generate(prompt, temperature=temp)
                if self._quality_check(prompt, response):
                    data.append({"prompt": prompt, "response": response})
        
        # 3. 难度梯度
        for difficulty in ["easy", "medium", "hard"]:
            hard_prompts = self._filter_by_difficulty(expanded_prompts, difficulty)
            for prompt in hard_prompts:
                response = self.teacher.generate(prompt)
                data.append({"prompt": prompt, "response": response, "difficulty": difficulty})
        
        # 4. 去重
        data = self._deduplicate(data)
        
        return data[:target_count]