def training_free_grpo(model, dataset, iterations=100):
    """
    Training-Free GRPO算法实现
    
    参数:
        model: 大语言模型
        dataset: 训练数据集
        iterations: 迭代次数
    
    返回:
        experience_db: 优化后的经验知识库
    """
    experience_db = initialize_experience_db()
    
    for iteration in range(iterations):
        for query in dataset:
            # 1. 生成多个候选答案
            candidates = generate_multiple_answers(model, query, num_candidates=5)
            
            # 2. 评估答案质量
            rewards = evaluate_answers(candidates)
            
            # 3. 语义比较分析
            semantic_analysis = analyze_semantic_advantages(model, candidates, rewards)
            
            # 4. 更新经验库
            experience_db = update_experience_db(experience_db, semantic_analysis)
    
    # 5. 经验库优化
    experience_db = optimize_experience_db(model, experience_db)
    
    return experience_db

def generate_multiple_answers(model, query, num_candidates=5):
    """生成多个候选答案"""
    answers = []
    for _ in range(num_candidates):
        answer = model.generate(query + f"\n请尝试第{_+1}种解法：")
        answers.append(answer)
    return answers

def analyze_semantic_advantages(model, candidates, rewards):
    """分析语义优势"""
    # 找出最佳和最差的答案
    best_idx = rewards.index(max(rewards))
    worst_idx = rewards.index(min(rewards))
    
    # 让模型分析差异
    analysis_prompt = f"""
    请分析以下两个答案的差异，并总结出改进经验：
    
    最佳答案（得分{rewards[best_idx]}）：
    {candidates[best_idx]}
    
    最差答案（得分{rewards[worst_idx]}）：
    {candidates[worst_idx]}
    
    请总结出为什么最佳答案更好，以及如何避免最差答案的问题。
    """
    
    semantic_experience = model.generate(analysis_prompt)
    return semantic_experience