diff --git a/train_cispo.py b/train_cispo.py
new file mode 100644
index 0000000..6c0ebd6
--- /dev/null
+++ b/train_cispo.py
@@ -0,0 +1,343 @@
+import os
+import sys
+
+__package__ = "trainer"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import argparse
+import re
+import gc
+import warnings
+import torch
+import torch.distributed as dist
+from transformers import AutoTokenizer
+from contextlib import nullcontext
+from torch import optim
+from torch.nn.parallel import DistributedDataParallel
+from torch.utils.data import DataLoader, DistributedSampler
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from transformers import AutoModel
+from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
+from dataset.lm_dataset import RLAIFDataset
+from trainer.trainer_utils import Logger, is_main_process, lm_checkpoint, init_distributed_mode, setup_seed, SkipBatchSampler, init_model
+
+warnings.filterwarnings('ignore')
+
+
+def calculate_rewards(prompts, responses, reward_model, reward_tokenizer):
+    """整合所有奖励函数计算总奖励"""
+    def reasoning_model_reward(rewards):
+        pattern = r"^<think>\n.*?\n</think>\n<answer>\n.*?\n</answer>$"
+        pattern2 = r"^<think>\n.*?\n</think>\n\n<answer>\n.*?\n</answer>$"
+        matches_pattern = [re.match(pattern, response, re.S) for response in responses]
+        matches_pattern2 = [re.match(pattern2, response, re.S) for response in responses]
+
+        format_rewards = []
+        for match_pattern, match_pattern2 in zip(matches_pattern, matches_pattern2):
+            if match_pattern or match_pattern2:
+                format_rewards.append(0.5)
+            else:
+                format_rewards.append(0.0)
+        rewards += torch.tensor(format_rewards, device=args.device)
+
+        def mark_num(text):
+            reward = 0
+            if text.count("<think>") == 1: reward += 0.25
+            if text.count("</think>") == 1: reward += 0.25
+            if text.count("<answer>") == 1: reward += 0.25
+            if text.count("</answer>") == 1: reward += 0.25
+            return reward
+
+        mark_rewards = [mark_num(response) for response in responses]
+        rewards += torch.tensor(mark_rewards, device=args.device)
+        return rewards
+
+    rewards = torch.zeros(len(responses), device=args.device)
+    if args.reasoning == 1:
+        rewards = reasoning_model_reward(rewards)
+
+    with torch.no_grad():
+        reward_model_scores = []
+        batch_size = len(prompts)
+        scale = 3.0
+
+        for i in range(batch_size):
+            for j in range(args.num_generations):
+                response_idx = i * args.num_generations + j
+                response = responses[response_idx]
+                prompt = prompts[i]
+
+                pattern = r"<\|im_start\|>(system|user|assistant)\s+(.*?)<\|im_end\|>"
+                matches = re.findall(pattern, prompt, re.DOTALL)
+                messages = [{"role": role, "content": content.strip()} for role, content in matches]
+
+                tmp_chat = messages + [{"role": "assistant", "content": response}]
+                score = reward_model.get_score(reward_tokenizer, tmp_chat)
+                score = max(min(score, scale), -scale)
+
+                if args.reasoning == 1:
+                    answer_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
+                    if answer_match:
+                        answer_content = answer_match.group(1).strip()
+                        tmp_chat = messages + [{"role": "assistant", "content": answer_content}]
+                        answer_score = reward_model.get_score(reward_tokenizer, tmp_chat)
+                        answer_score = max(min(answer_score, scale), -scale)
+                        score = score * 0.4 + answer_score * 0.6
+
+                reward_model_scores.append(score)
+
+        reward_model_scores = torch.tensor(reward_model_scores, device=args.device)
+        rewards += reward_model_scores
+
+    return rewards
+
+
+def get_per_token_logps(mdl, input_ids, n_keep):
+    """计算每个token的log概率"""
+    # CISPO 需要多次计算 logps，必须保留梯度
+    if not mdl.training:
+         with torch.no_grad():
+            logits = mdl(input_ids, logits_to_keep=n_keep + 1).logits[:, :-1, :]
+    else:
+        logits = mdl(input_ids, logits_to_keep=n_keep + 1).logits[:, :-1, :]
+    
+    per_token_logps = []
+    for logits_row, ids_row in zip(logits, input_ids[:, -n_keep:]):
+        per_token_logps.append(torch.gather(logits_row.log_softmax(dim=-1), 1, ids_row.unsqueeze(1)).squeeze(1))
+    return torch.stack(per_token_logps)
+
+def cispo_train_epoch(epoch, loader, iters, ref_model, reward_model, reward_tokenizer, start_step=0, wandb=None):
+    for step, batch in enumerate(loader, start=start_step + 1):
+        prompts = batch['prompt']
+        prompt_inputs = tokenizer(prompts, return_tensors="pt", padding=True, return_token_type_ids=False,
+                                  padding_side="left", add_special_tokens=False).to(args.device)
+        
+        if args.max_seq_len:
+            prompt_inputs["input_ids"] = prompt_inputs["input_ids"][:, -args.max_seq_len:]
+            prompt_inputs["attention_mask"] = prompt_inputs["attention_mask"][:, -args.max_seq_len:]
+
+        # ========== 1. 采样 (Sampling) ==========
+        with torch.no_grad():
+            model_for_gen = model.module if isinstance(model, DistributedDataParallel) else model
+            outputs = model_for_gen.generate(
+                **prompt_inputs, max_new_tokens=args.max_gen_len, do_sample=True, temperature=0.8,
+                num_return_sequences=args.num_generations, pad_token_id=tokenizer.pad_token_id)
+
+        completion_ids = outputs[:, prompt_inputs["input_ids"].size(1):]
+        
+        # ========== 2. 准备 Reference 和 Old Policy ==========
+        with torch.no_grad():
+            # 计算参考模型的 logps (用于 KL 惩罚)
+            ref_per_token_logps = get_per_token_logps(ref_model, outputs, completion_ids.size(1))
+            # 计算当前（旧）策略的 logps (用于计算 Ratio，在 Inner Loop 中保持不变)
+            old_per_token_logps = get_per_token_logps(model, outputs, completion_ids.size(1))
+
+        # ========== 3. 计算奖励 (Reward & Advantage) ==========
+        completions = tokenizer.batch_decode(completion_ids, skip_special_tokens=True)
+        rewards = calculate_rewards(prompts, completions, reward_model, reward_tokenizer).to(args.device)
+
+        grouped_rewards = rewards.view(-1, args.num_generations)
+        mean_r = grouped_rewards.mean(dim=1).repeat_interleave(args.num_generations)
+        std_r = grouped_rewards.std(dim=1).repeat_interleave(args.num_generations)
+        
+        # 归一化优势
+        advantages = torch.clamp((rewards - mean_r) / (std_r + 1e-4), -10, 10)
+        # 此处可以做全局归一化
+        # advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
+
+        # 准备 Mask
+        is_eos = completion_ids == tokenizer.eos_token_id
+        eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=args.device)
+        eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
+        completion_mask = (torch.arange(is_eos.size(1), device=args.device).expand(is_eos.size(0), -1) <= eos_idx.unsqueeze(1)).int()
+
+        # ========== 4. CISPO Inner Loop (多步更新) ==========
+        # 循环多次，Ratio 才会发生变化，Clipping 才会生效
+        for _ in range(args.ppo_epochs):
+            # 获取当前模型的 logps (带有梯度)
+            per_token_logps = get_per_token_logps(model, outputs, completion_ids.size(1))
+            
+            # 计算 Ratio
+            # ratio = exp(curr - old)
+            ratio = torch.exp(per_token_logps - old_per_token_logps)
+
+
+            # 计算裁剪统计信息 (用于日志记录)
+            # 找到高于上限和低于下限的部分
+            over_upper = (ratio > (1.0 + args.clip_ratio)).float()
+            under_lower = (ratio < (1.0 - args.clip_ratio)).float()
+            
+            # 计算总的被裁剪比例 (Token 级别)
+            clip_fraction = (over_upper + under_lower).mean().item()
+            
+            # 分别查看上溢和下溢（有助于分析模型是变激进了还是变保守了）
+            upper_fraction = over_upper.mean().item()
+            lower_fraction = under_lower.mean().item()
+
+
+            # --- CISPO 核心逻辑 ---
+            # 1. 计算裁剪后的权重系数 (Weight Clipping)
+            # 关键：必须 .detach()，使其变为常数系数，而不是目标函数的一部分
+            clipped_ratio = torch.clamp(ratio, 1.0 - args.clip_ratio, 1.0 + args.clip_ratio).detach()
+            
+            # 2. 计算 KL 惩罚 (Token-level)
+            kl_div = ref_per_token_logps - per_token_logps
+            per_token_kl = torch.exp(kl_div) - kl_div - 1
+            
+            # 3. 计算 Loss
+            # 公式: L = - (Clipped_Weight * log_pi * A) + beta * KL
+            # 这里利用 pytorch 的自动求导：nabla(log_pi) * A * Weight
+            cispo_loss = - (clipped_ratio * per_token_logps * advantages.unsqueeze(1)) + args.beta * per_token_kl
+            # --- CISPO 核心逻辑 End ---
+
+            # Mask 和 聚合
+            loss = ((cispo_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean() / args.accumulation_steps
+            
+            loss.backward()
+
+        if (step + 1) % args.accumulation_steps == 0:
+            if args.grad_clip > 0:
+                torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
+            optimizer.step()
+            scheduler.step()
+            optimizer.zero_grad()
+            torch.cuda.empty_cache()
+
+        if step % args.log_interval == 0 or step == iters:
+            # 记录日志
+            policy_loss_val = loss.item() * args.accumulation_steps
+            avg_reward_val = rewards.mean().item()
+            avg_len_val = completion_mask.sum(dim=1).float().mean().item()
+            current_lr = optimizer.param_groups[0]['lr']
+
+            Logger(f'Epoch: {epoch+1}, Step: {step}/{iters}, '
+                   f'CISPO Loss: {policy_loss_val:.6f}, Reward: {avg_reward_val:.6f}, '
+                   f'Avg Response Len: {avg_len_val:.2f}, LR: {current_lr:.2e}')
+
+            if wandb and is_main_process():
+                wandb.log({
+                    "policy_loss": policy_loss_val,
+                    "reward": avg_reward_val,
+                    "avg_response_len": avg_len_val,
+                    "advantages_mean": advantages.mean().item(),
+                    "learning_rate": current_lr,
+                    "ratio": ratio.mean().item(),
+                    "cispo/clip_fraction": clip_fraction,      # 总裁剪率
+                    "cispo/upper_clip_fraction": upper_fraction, # 上裁剪率（由于当前动作概率大幅增加导致）
+                    "cispo/lower_clip_fraction": lower_fraction, # 下裁剪率（由于当前动作概率大幅降低导致）
+                })
+
+        # 保存模型的代码保持不变
+        if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
+            model.eval()
+            moe_suffix = '_moe' if lm_config.use_moe else ''
+            ckp = f'{args.save_dir}/{args.save_weight}_{lm_config.hidden_size}{moe_suffix}.pth'
+            state_dict = model.module.state_dict() if isinstance(model, DistributedDataParallel) else model.state_dict()
+            torch.save({k: v.half().cpu() for k, v in state_dict.items()}, ckp)
+            lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer, 
+                          epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints', scheduler=scheduler)
+            model.train()
+            del state_dict
+
+        del prompt_inputs, outputs, completion_ids, per_token_logps, ref_per_token_logps, old_per_token_logps
+        del completions, rewards, grouped_rewards, mean_r, std_r, advantages, completion_mask
+        torch.cuda.empty_cache()
+        gc.collect()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="MiniMind CISPO (Clipped Importance Sampling Policy Optimization)")
+    # 原有参数保持不变
+    parser.add_argument("--save_dir", type=str, default="../out", help="模型保存目录")
+    parser.add_argument('--save_weight', default='cispo', type=str, help="保存权重的前缀名")
+    parser.add_argument("--epochs", type=int, default=1, help="训练轮数")
+    parser.add_argument("--batch_size", type=int, default=2, help="batch size")
+    parser.add_argument("--learning_rate", type=float, default=8e-8, help="初始学习率")
+    parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", help="训练设备")
+    parser.add_argument("--dtype", type=str, default="bfloat16", help="混合精度类型")
+    parser.add_argument("--num_workers", type=int, default=1, help="数据加载线程数")
+    parser.add_argument("--accumulation_steps", type=int, default=1, help="梯度累积步数")
+    parser.add_argument("--grad_clip", type=float, default=1.0, help="梯度裁剪阈值")
+    parser.add_argument("--log_interval", type=int, default=1, help="日志打印间隔")
+    parser.add_argument("--save_interval", type=int, default=10, help="模型保存间隔")
+    parser.add_argument('--hidden_size', default=512, type=int, help="隐藏层维度")
+    parser.add_argument('--num_hidden_layers', default=8, type=int, help="隐藏层数量")
+    parser.add_argument('--use_moe', default=0, type=int, choices=[0, 1], help="是否使用MoE架构（0=否，1=是）")
+    parser.add_argument('--max_seq_len', default=66, type=int, help="Prompt最大长度")
+    parser.add_argument("--max_gen_len", type=int, default=1536, help="生成的最大长度")
+    parser.add_argument("--data_path", type=str, default="../dataset/rlaif-mini.jsonl", help="RLAIF数据路径")
+    parser.add_argument("--num_generations", type=int, default=8, help="每个prompt生成的样本数")
+    parser.add_argument("--beta", type=float, default=0.02, help="KL惩罚系数")
+    parser.add_argument("--reasoning", type=int, default=1, choices=[0, 1], help='推理模型类型')
+    parser.add_argument("--reward_model_path", type=str, default="../../internlm2-1_8b-reward", help="Reward模型路径")
+    parser.add_argument('--from_resume', default=0, type=int, choices=[0, 1], help="是否自动检测&续训")
+    parser.add_argument("--use_wandb", action="store_true", help="是否使用wandb")
+    parser.add_argument("--wandb_project", type=str, default="MiniMind-CISPO", help="wandb项目名")
+    
+    # === CISPO 新增参数 ===
+    parser.add_argument("--clip_ratio", type=float, default=0.2, help="CISPO/PPO 裁剪系数")
+    parser.add_argument("--ppo_epochs", type=int, default=1, help="每个Batch的更新次数 (Inner Loop)")
+    
+    args = parser.parse_args()
+
+    # 后续初始化代码与原 GRPO 代码一致，只需将 train_epoch 调用替换为 cispo_train_epoch
+    # ========== 1. 初始化环境 ==========
+    local_rank = init_distributed_mode()
+    if dist.is_initialized(): args.device = f"cuda:{local_rank}"
+    setup_seed(42 + (dist.get_rank() if dist.is_initialized() else 0))
+    
+    os.makedirs(args.save_dir, exist_ok=True)
+    lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers,
+                               max_seq_len=args.max_seq_len + args.max_gen_len, use_moe=bool(args.use_moe))
+    ckp_data = lm_checkpoint(lm_config, weight=args.save_weight, save_dir='../checkpoints') if args.from_resume==1 else None
+    
+    device_type = "cuda" if "cuda" in args.device else "cpu"
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+    autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
+    
+    wandb = None
+    if args.use_wandb and is_main_process():
+        import swanlab as wandb
+        wandb_id = ckp_data.get('wandb_id') if ckp_data else None
+        resume = 'must' if wandb_id else None
+        wandb_run_name = f"MiniMind-CISPO-Epoch-{args.epochs}"
+        wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume, mode="local")
+    
+    base_weight = "reason" if args.reasoning == 1 else "full_sft"
+    model, tokenizer = init_model(lm_config, base_weight, device=args.device)
+    ref_model, _ = init_model(lm_config, base_weight, device=args.device)
+    ref_model = ref_model.eval().requires_grad_(False)
+    
+    reward_model = AutoModel.from_pretrained(args.reward_model_path, torch_dtype=torch.float16, trust_remote_code=True)
+    reward_model = reward_model.to(args.device).eval().requires_grad_(False)
+    reward_tokenizer = AutoTokenizer.from_pretrained(args.reward_model_path, trust_remote_code=True)
+    
+    train_ds = RLAIFDataset(args.data_path, tokenizer, max_length=lm_config.max_seq_len)
+    train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
+    optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
+    loader_for_count = DataLoader(train_ds, batch_size=args.batch_size, sampler=train_sampler)
+    iters = len(loader_for_count)
+    total_optimizer_steps = (iters // args.accumulation_steps) * args.epochs * args.ppo_epochs # 注意这里total steps变了
+    scheduler = CosineAnnealingLR(optimizer, T_max=total_optimizer_steps, eta_min=args.learning_rate / 10)
+    
+    start_epoch, start_step = 0, 0
+    if ckp_data:
+        model.load_state_dict(ckp_data['model'])
+        optimizer.load_state_dict(ckp_data['optimizer'])
+        scheduler.load_state_dict(ckp_data['scheduler'])
+        start_epoch = ckp_data['epoch']
+        start_step = ckp_data.get('step', 0)
+    
+    if dist.is_initialized():
+        model._ddp_params_and_buffers_to_ignore = {"freqs_cos", "freqs_sin"}
+        model = DistributedDataParallel(model, device_ids=[local_rank])
+    
+    for epoch in range(start_epoch, args.epochs):
+        train_sampler and train_sampler.set_epoch(epoch)
+        if epoch == start_epoch and start_step > 0:
+            batch_sampler = SkipBatchSampler(train_sampler or range(len(train_ds)), args.batch_size, start_step + 1)
+            loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
+            cispo_train_epoch(epoch, loader, len(loader) + start_step + 1, ref_model, reward_model, reward_tokenizer, start_step, wandb)
+        else:
+            loader = DataLoader(train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=(train_sampler is None), num_workers=args.num_workers, sampler=train_sampler)
+            cispo_train_epoch(epoch, loader, len(loader), ref_model, reward_model, reward_tokenizer, 0, wandb)