08. 2026 前沿论文解读¶

# GRPO 核心算法伪代码
def grpo_train(policy_model, prompts, reward_fn, G=16, epsilon=0.2, beta=0.01):
    """
    GRPO: Group Relative Policy Optimization

    核心思想：
    1. 对每个问题采样G个候选回答
    2. 用规则化奖励函数打分（无需Reward Model）
    3. 组内标准化计算优势值
    4. PPO-clip式策略优化
    """
    for prompt in prompts:
        # 组采样
        responses = [policy_model.generate(prompt) for _ in range(G)]

        # 规则化奖励（如：答案正确性）
        rewards = [reward_fn(prompt, resp) for resp in responses]

        # 组内标准化
        mean_r = sum(rewards) / G
        std_r = (sum((r - mean_r)**2 for r in rewards) / G) ** 0.5
        advantages = [(r - mean_r) / (std_r + 1e-8) for r in rewards]

        # 策略优化（PPO-clip）
        for resp, adv in zip(responses, advantages):
            ratio = policy_model.log_prob(resp, prompt).exp()
            clipped = torch.clamp(ratio, 1 - epsilon, 1 + epsilon)
            loss = -torch.min(ratio * adv, clipped * adv)

            # KL散度惩罚
            loss += beta * kl_divergence(policy_model, ref_model)
            loss.backward()

    optimizer.step()

class DAPOTrainer:
    """
    DAPO: GRPO的改进版本

    主要改进：
    1. 解耦裁剪：epsilon_low != epsilon_high
    2. 动态采样阈值delta
    3. 熵正则化系数entropy_coef
    """
    def __init__(self, epsilon_low=0.2, epsilon_high=0.28, delta=1.5, entropy_coef=0.01):
        self.epsilon_low = epsilon_low    # 下界裁剪（保守）
        self.epsilon_high = epsilon_high  # 上界裁剪（更大，鼓励探索）
        self.delta = delta                # 动态采样阈值
        self.entropy_coef = entropy_coef  # 熵正则化系数

    def compute_loss(self, policy, ref_policy, prompt, responses, rewards):
        # 组内标准化
        advantages = self.normalize_advantages(rewards)

        total_loss = 0
        for resp, adv in zip(responses, advantages):
            log_prob = policy.log_prob(resp, prompt)
            ref_log_prob = ref_policy.log_prob(resp, prompt)

            ratio = (log_prob - ref_log_prob).exp()

            # 解耦裁剪（关键改进）
            if adv > 0:
                # 正优势：使用更大的上界，鼓励探索
                clipped = torch.clamp(ratio, 1 - self.epsilon_low, 1 + self.epsilon_high)
            else:
                # 负优势：使用更小的下界，快速淘汰差回答
                clipped = torch.clamp(ratio, 1 - self.epsilon_high, 1 + self.epsilon_low)

            policy_loss = -torch.min(ratio * adv, clipped * adv)

            # 熵正则化（鼓励多样性）
            entropy = -log_prob.exp() * log_prob
            entropy_bonus = self.entropy_coef * entropy

            total_loss += policy_loss - entropy_bonus

        return total_loss / len(responses)

    def dynamic_sampling(self, advantages):
        """动态采样：只保留优势值超过阈值的样本"""
        return [adv for adv in advantages if abs(adv) > self.delta]

class RLVRTrainer:
    """
    RLVR: Reinforcement Learning with Verifiable Rewards

    核心组件：
    1. Verifier（验证器）：确定性判断答案正确性
    2. Policy（策略模型）：生成回答
    3. Reward Shaping（奖励塑形）：部分正确也给部分分
    """
    def __init__(self, policy_model, verifier, reward_shaping=True):
        self.policy = policy_model
        self.verifier = verifier  # 确定性验证器
        self.reward_shaping = reward_shaping

    def compute_reward(self, prompt, response):
        """计算可验证奖励"""
        # 基础奖励：答案正确性
        base_reward = self.verifier.verify(prompt, response)

        if not self.reward_shaping:
            return base_reward

        # 奖励塑形：部分正确也给部分分
        shaped_reward = 0.0

        # 格式正确（如：有最终答案）
        if self.verifier.check_format(response):
            shaped_reward += 0.1

        # 中间步骤正确
        steps = self.verifier.extract_steps(response)
        correct_steps = sum(self.verifier.verify_step(s) for s in steps)
        shaped_reward += 0.3 * (correct_steps / max(len(steps), 1))

        # 最终答案正确
        if base_reward == 1.0:
            shaped_reward += 0.6

        return shaped_reward

    def train_step(self, prompts):
        for prompt in prompts:
            # 采样多个回答
            responses = [self.policy.generate(prompt) for _ in range(8)]

            # 计算可验证奖励
            rewards = [self.compute_reward(prompt, resp) for resp in responses]

            # GRPO式优化
            self.grpo_update(prompt, responses, rewards)


class MathVerifier:
    """数学验证器示例"""
    def verify(self, prompt, response):
        # 提取最终答案
        predicted = self.extract_answer(response)
        ground_truth = self.get_ground_truth(prompt)

        # 数值比较（允许小误差）
        try:
            return 1.0 if abs(predicted - ground_truth) < 1e-6 else 0.0
        except:
            return 0.0

    def check_format(self, response):
        # 检查是否有明确的最终答案格式
        return "\\boxed{" in response or "答案是" in response

    def extract_steps(self, response):
        # 提取推理步骤
        return response.split("\n")


class CodeVerifier:
    """代码验证器示例"""
    def __init__(self, test_cases, timeout=5):
        self.test_cases = test_cases
        self.timeout = timeout

    def verify(self, prompt, response):
        code = self.extract_code(response)

        passed = 0
        for test_input, expected_output in self.test_cases:
            try:
                actual = self.execute_code(code, test_input)
                if actual == expected_output:
                    passed += 1
            except:
                pass

        return passed / len(self.test_cases)

class ProRLTrainer:
    """
    ProRL: Prolonged Reinforcement Learning

    关键配置：
    1. 学习率衰减策略
    2. 经验回放缓冲区
    3. 课程学习
    """
    def __init__(self, policy, total_steps=100000):
        self.policy = policy
        self.total_steps = total_steps
        self.replay_buffer = ReplayBuffer(capacity=100000)
        self.curriculum = CurriculumScheduler()

    def train(self):
        for step in range(self.total_steps):
            # 课程学习：逐步增加任务难度
            task_difficulty = self.curriculum.get_difficulty(step)
            tasks = self.sample_tasks(difficulty=task_difficulty)

            # 收集经验
            for task in tasks:
                trajectory = self.policy.rollout(task)
                self.replay_buffer.add(trajectory)

            # 从回放缓冲区采样训练
            batch = self.replay_buffer.sample(batch_size=32)

            # 学习率衰减
            lr = self.get_lr(step)

            # 策略更新
            self.update_policy(batch, lr)

            # 定期评估
            if step % 1000 == 0:
                self.evaluate()

    def get_lr(self, step):
        """余弦退火学习率"""
        warmup_steps = 5000
        if step < warmup_steps:
            return 1e-5 * (step / warmup_steps)

        progress = (step - warmup_steps) / (self.total_steps - warmup_steps)
        return 1e-5 * 0.5 * (1 + np.cos(np.pi * progress))

class RULERTrainer:
    """
    RULER: Relative Universal LLM-Elicited Rewards

    核心组件：
    1. LLM Judge：评估回答质量
    2. Relative Reward：相对排序而非绝对打分
    3. Self-Consistency：多次评估取一致
    """
    def __init__(self, policy, judge_model, consistency_samples=3):
        self.policy = policy
        self.judge = judge_model
        self.consistency_samples = consistency_samples

    def compute_relative_reward(self, prompt, response_a, response_b):
        """计算相对奖励：A比B好多少"""
        judge_prompt = f"""
        问题：{prompt}

        回答A：{response_a}

        回答B：{response_b}

        请判断哪个回答更好。输出格式：
        - 如果A明显更好：输出" A"
        - 如果B明显更好：输出" B"
        - 如果差不多：输出" TIE"
        """

        # 多次评估取一致
        votes = []
        for _ in range(self.consistency_samples):
            result = self.judge.generate(judge_prompt)
            if "A" in result and "B" not in result:
                votes.append(1)  # A胜
            elif "B" in result and "A" not in result:
                votes.append(-1)  # B胜
            else:
                votes.append(0)  # 平局

        # 多数投票
        return sum(votes) / len(votes)

    def train_step(self, prompts):
        for prompt in prompts:
            # 采样多个回答
            responses = [self.policy.generate(prompt) for _ in range(4)]

            # 两两比较计算相对奖励
            rewards = [0.0] * len(responses)
            for i in range(len(responses)):
                for j in range(i + 1, len(responses)):
                    rel_reward = self.compute_relative_reward(
                        prompt, responses[i], responses[j]
                    )
                    rewards[i] += rel_reward
                    rewards[j] -= rel_reward

            # 标准化
            rewards = torch.tensor(rewards)
            rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-8)

            # 策略更新
            self.update_policy(prompt, responses, rewards)

class ARTTrainer:
    """
    ART: Agent Reinforcement Trainer

    特点：
    1. 原生支持MCP工具调用
    2. 多轮对话训练
    3. 工具使用奖励
    """
    def __init__(self, policy, mcp_tools, max_turns=10):
        self.policy = policy
        self.tools = mcp_tools
        self.max_turns = max_turns

    def rollout(self, task):
        """执行完整任务轨迹"""
        trajectory = []
        context = task.description

        for turn in range(self.max_turns):
            # 模型决策
            action = self.policy.act(context)

            if action.type == "tool_call":
                # 执行MCP工具
                tool_result = self.execute_mcp_tool(
                    action.tool_name, 
                    action.tool_args
                )

                # 记录轨迹
                trajectory.append({
                    "turn": turn,
                    "action": action,
                    "result": tool_result,
                    "reward": self.compute_step_reward(action, tool_result)
                })

                # 更新上下文
                context += f"\n工具调用：{action.tool_name}\n结果：{tool_result}"

            elif action.type == "answer":
                # 任务完成
                trajectory.append({
                    "turn": turn,
                    "action": action,
                    "final": True
                })
                break

        # 计算总奖励
        total_reward = self.compute_trajectory_reward(trajectory, task)

        return trajectory, total_reward

    def compute_step_reward(self, action, result):
        """单步奖励"""
        reward = 0.0

        # 工具调用成功
        if result.success:
            reward += 0.1

        # 调用相关工具
        if self.is_relevant_tool(action.tool_name, action.context):
            reward += 0.1

        # 结果有用
        if self.is_useful_result(result):
            reward += 0.1

        return reward

    def compute_trajectory_reward(self, trajectory, task):
        """轨迹总奖励"""
        # 基础奖励：任务是否完成
        success = self.verify_task_completion(trajectory, task)
        base_reward = 1.0 if success else 0.0

        # 效率奖励：步数越少越好
        steps = len(trajectory)
        efficiency_reward = max(0, 1 - (steps - 3) / 10)

        # 工具使用奖励
        tool_reward = sum(step["reward"] for step in trajectory)

        return base_reward + 0.3 * efficiency_reward + 0.2 * tool_reward

    def execute_mcp_tool(self, tool_name, args):
        """执行MCP工具"""
        tool = self.tools.get(tool_name)
        if tool:
            return tool.execute(**args)
        return ToolResult(success=False, error="Tool not found")

from anthropic import Anthropic

client = Anthropic(api_key=api_key)

# Opus 4.6：更适合直接使用 adaptive thinking
opus_response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=4096,
    messages=[{"role": "user", "content": "分析这个复杂的系统设计问题。"}]
)

# Sonnet 4.6：如果你要显式设置 thinking budget，用这个型号更合适
sonnet_response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=16000,
    thinking={
        "type": "enabled",
        "budget_tokens": 10000
    },
    messages=[{"role": "user", "content": "请逐步分析这个复杂的系统设计问题。"}]
)