LLM持续学习实践：让模型与时俱进

# 灾难性遗忘示例
model = train_on_task_A(model, data_A)  # 学会任务A
model = train_on_task_B(model, data_B)  # 学会任务B，但忘了任务A

class ContinualLearning:
    # 策略1: 经验回放
    def replay_based(self, new_data, old_data_sample):
        """混入旧数据"""
        mixed = new_data + old_data_sample
        return self.train(mixed)
    
    # 策略2: 弹性权重巩固（EWC）
    def ewc(self, model, new_data, old_params, fisher_matrix):
        """EWC正则化"""
        for name, param in model.named_parameters():
            loss = task_loss + lambda_ * (fisher_matrix[name] * (param - old_params[name])**2).sum()
    
    # 策略3: LoRA适配器
    def lora_per_task(self, base_model, task_data):
        """每个任务一个LoRA适配器"""
        lora = LoRA(r=8)
        lora.train(task_data)
        return lora  # base_model不变

# 对于事实性知识更新，RAG通常是更好的选择
# 不需要修改模型参数，只需更新知识库

class IncrementalFineTuner:
    async def incremental_update(self, model, new_knowledge):
        """增量知识更新"""
        # 1. 构建增量数据
        incremental_data = self.format_knowledge(new_knowledge)
        
        # 2. 混入旧数据（防遗忘）
        replay_data = self.sample_old_data(ratio=0.3)
        train_data = incremental_data + replay_data
        
        # 3. 小学习率微调
        config = SFTConfig(
            learning_rate=1e-6,  # 比初始SFT小10倍
            num_train_epochs=1,
        )
        
        return self.train(model, train_data, config)

class MultiLoRAManager:
    def __init__(self, base_model):
        self.base_model = base_model
        self.lora_adapters = {}  # {domain: lora_adapter}
    
    async def update_domain(self, domain, new_data):
        """更新特定领域的LoRA"""
        if domain in self.lora_adapters:
            # 在现有LoRA基础上继续训练
            lora = self.lora_adapters[domain]
        else:
            # 创建新LoRA
            lora = LoRA(r=8)
        
        lora.train(new_data)
        self.lora_adapters[domain] = lora
    
    async def generate(self, prompt, domain=None):
        """生成时选择合适的LoRA"""
        if domain and domain in self.lora_adapters:
            self.base_model.load_adapter(self.lora_adapters[domain])
        return await self.base_model.generate(prompt)

class ContinualLearningEvaluator:
    async def evaluate(self, model, old_benchmarks, new_benchmarks):
        """评估持续学习效果"""
        results = {
            "old_performance": {},  # 旧任务性能（遗忘程度）
            "new_performance": {},  # 新任务性能（学习效果）
            "transfer": {}          # 知识迁移效果
        }
        
        for bench in old_benchmarks:
            results["old_performance"][bench] = await run_benchmark(model, bench)
        
        for bench in new_benchmarks:
            results["new_performance"][bench] = await run_benchmark(model, bench)
        
        # 遗忘率
        forgetting = 1 - (results["old_performance"]["avg"] / baseline_old_performance)
        results["forgetting_rate"] = forgetting
        
        return results

事实性知识更新 → RAG（不修改模型）
领域适配 → LoRA微调
能力提升 → SFT + DPO
紧急修正 → 小数据快速微调

class ModelVersionManager:
    def __init__(self):
        self.versions = {}
    
    def save_version(self, model, version_id, metadata):
        """保存模型版本"""
        self.versions[version_id] = {
            "model": model,
            "metadata": metadata,
            "timestamp": time.time(),
            "performance": metadata.get("performance", {})
        }
    
    def rollback(self, version_id):
        """回滚到之前的版本"""
        return self.versions[version_id]["model"]

# 持续学习监控指标
metrics = {
    "new_task_accuracy": "新任务的准确率",
    "old_task_accuracy": "旧任务的准确率（遗忘指标）",
    "general_capability": "通用能力（不应下降）",
    "safety_score": "安全分数（不应下降）",
    "latency": "推理延迟（不应增加）"
}