Merge 138c367ddb into 512eed0b65

README.md

@@ -765,16 +765,16 @@ LoRA 是一种常见的参数高效微调（Parameter-Efficient Fine-Tuning, PEF
 ```bash
 # train_lora.py 在 CPU 上通常也能比较轻快地完成
 # 方式1
-torchrun --nproc_per_node 1 train_lora.py
+cd trainer && torchrun --nproc_per_node 1 train_lora.py
 # 方式2
-python train_lora.py
+cd trainer && python train_lora.py
 ```
 
 > 训练后的模型权重文件默认每隔`save_interval步`保存为: `lora_xxx_*.pth`（*为模型具体dimension，每次保存时新文件会覆盖旧文件）
 
 
 LoRA 很适合处理“如何在尽量保留通用能力的前提下，让模型快速适应私有领域或垂直场景”这类问题。例如基础模型医学知识不足时，就可以在原有模型之上叠加一层面向医疗场景的 LoRA 权重，以较小代价获得更好的领域表现。
-通常只需要准备同样的多轮对话格式数据，放置到 `lora_xxx.jsonl`，再执行 `python train_lora.py`，即可得到新的 `LoRA` 模型权重。
+通常只需要准备同样的多轮对话格式数据，放置到 `lora_xxx.jsonl`，再从仓库根目录执行 `cd trainer && python train_lora.py`，即可得到新的 `LoRA` 模型权重。
 
 例1：垂域数据
 

README_en.md

@@ -765,16 +765,16 @@ Its core idea is to introduce low-rank incremental branches alongside the origin
 ```bash
 # train_lora.py can usually be completed fairly quickly even on CPU
 # Method 1
-torchrun --nproc_per_node 1 train_lora.py
+cd trainer && torchrun --nproc_per_node 1 train_lora.py
 # Method 2
-python train_lora.py
+cd trainer && python train_lora.py
 ```
 
 > The trained model weight files are saved by default every `save_interval steps` as: `lora_xxx_*.pth` (* is the specific model dimension, each save overwrites the previous file)
 
 
 LoRA is well-suited for handling problems like "how to let the model quickly adapt to private domains or vertical scenarios while preserving general capabilities as much as possible." For example, when the base model lacks medical knowledge, a medical-oriented LoRA weight layer can be stacked on top of the original model to achieve better domain performance at relatively small cost.
-Usually you only need to prepare multi-turn dialogue format data in the same way, place it in `lora_xxx.jsonl`, and then run `python train_lora.py` to obtain new `LoRA` model weights.
+Usually you only need to prepare multi-turn dialogue format data in the same way, place it in `lora_xxx.jsonl`, and then run `cd trainer && python train_lora.py` from the repository root to obtain new `LoRA` model weights.
 
 Example 1: Vertical domain data
 

scripts/chat_api.py

@@ -21,13 +21,19 @@ while True:
         extra_body={"chat_template_kwargs": {"open_thinking": True}, "reasoning_effort": "medium"} # 思考开关
     )
     if not stream:
+        if not response.choices or response.choices[0].message is None:
+            raise ValueError("LLM returned empty or filtered response")
         assistant_res = response.choices[0].message.content
         print('[A]: ', assistant_res)
     else:
         print('[A]: ', end='', flush=True)
         assistant_res = ''
         for chunk in response:
+            if not chunk.choices:
+                continue
             delta = chunk.choices[0].delta
+            if delta is None:
+                continue
             r = getattr(delta, 'reasoning_content', None) or ""
             c = delta.content or ""
             if r:

scripts/serve_openai_api.py

@@ -15,7 +15,7 @@ from threading import Thread
 from queue import Queue
 from fastapi import FastAPI, HTTPException
 from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
 from model.model_lora import apply_lora, load_lora
@@ -54,7 +54,7 @@ class ChatRequest(BaseModel):
     top_p: float = 0.92
     max_tokens: int = 8192
     stream: bool = True
-    tools: list = []
+    tools: list = Field(default_factory=list)
     open_thinking: bool = False
     chat_template_kwargs: dict = None
     
@@ -104,24 +104,28 @@ def parse_response(text):
 
 def generate_stream_response(messages, temperature, top_p, max_tokens, tools=None, open_thinking=False):
     try:
-        new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, tools=tools or None, open_thinking=open_thinking)[-max_tokens:]
+        new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, tools=tools or None, open_thinking=open_thinking)
         inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device)
 
         queue = Queue()
         streamer = CustomStreamer(tokenizer, queue)
 
         def _generate():
-            model.generate(
-                inputs.input_ids,
-                max_new_tokens=max_tokens,
-                do_sample=True,
-                temperature=temperature,
-                top_p=top_p,
-                attention_mask=inputs.attention_mask,
-                pad_token_id=tokenizer.pad_token_id,
-                eos_token_id=tokenizer.eos_token_id,
-                streamer=streamer
-            )
+            try:
+                model.generate(
+                    inputs.input_ids,
+                    max_new_tokens=max_tokens,
+                    do_sample=True,
+                    temperature=temperature,
+                    top_p=top_p,
+                    attention_mask=inputs.attention_mask,
+                    pad_token_id=tokenizer.pad_token_id,
+                    eos_token_id=tokenizer.eos_token_id,
+                    streamer=streamer
+                )
+            except Exception as e:
+                queue.put({"error": str(e)})
+                queue.put(None)
 
         Thread(target=_generate).start()
 
@@ -133,6 +137,9 @@ def generate_stream_response(messages, temperature, top_p, max_tokens, tools=Non
             text = queue.get()
             if text is None:
                 break
+            if isinstance(text, dict):
+                yield json.dumps(text, ensure_ascii=False)
+                continue
             full_text += text
 
             if not thinking_ended:
@@ -190,7 +197,7 @@ async def chat_completions(request: ChatRequest):
                 add_generation_prompt=True,
                 tools=request.tools or None,
                 open_thinking=request.get_open_thinking()
-            )[-request.max_tokens:]
+            )
             inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device)
             with torch.no_grad():
                 generated_ids = model.generate(

scripts/web_demo.py

@@ -339,8 +339,8 @@ def main():
         st.markdown(
             f'<div style="display: flex; justify-content: flex-end;"><div style="display: inline-block; margin: 10px 0; padding: 8px 12px 8px 12px; background-color: #3d4450; border-radius: 22px; color: white;">{prompt}</div></div>',
             unsafe_allow_html=True)
-        messages.append({"role": "user", "content": prompt[-st.session_state.max_new_tokens:]})
-        st.session_state.chat_messages.append({"role": "user", "content": prompt[-st.session_state.max_new_tokens:]})
+        messages.append({"role": "user", "content": prompt})
+        st.session_state.chat_messages.append({"role": "user", "content": prompt})
 
         placeholder = st.empty()
 

trainer/train_dapo.py

@@ -0,0 +1,421 @@
+import os
+import sys
+
+__package__ = "trainer"
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+import argparse
+import re
+import warnings
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+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.nn.utils import clip_grad_norm_
+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, args):
+    """
+    计算复杂奖励，包含格式奖励、模型打分以及 DAPO 特有的长度惩罚。
+    输入的 prompts/responses 已经是展开后的（batch_size * group_size）条目。
+    """
+    def reasoning_model_reward(rewards):
+        # 1. 结构化格式奖励
+        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,  r, re.S) for r in responses]
+        matches_pattern2 = [re.match(pattern2, r, re.S) for r in responses]
+        format_rewards = [0.5 if (m1 or m2) else 0.0
+                         for m1, m2 in zip(matches_pattern, matches_pattern2)]
+        rewards += torch.tensor(format_rewards, device=args.device)
+
+        # 2. 标签存在奖励
+        def mark_num(text):
+            reward = 0
+            for tag in ["<think>", "</think>", "<answer>", "</answer>"]:
+                if text.count(tag) == 1:
+                    reward += 0.25
+            return reward
+
+        mark_rewards = [mark_num(r) for r 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)
+
+    # 3. 超长惩罚 (Overlong Punishment - DAPO 特性)
+    len_penalty = []
+    for resp in responses:
+        curr_len = len(resp)
+        if curr_len > args.length_threshold:
+            penalty = -args.length_penalty * ((curr_len - args.length_threshold) / 100)
+            len_penalty.append(max(penalty, -1.0))
+        else:
+            len_penalty.append(0.0)
+    rewards += torch.tensor(len_penalty, device=args.device)
+
+    # 4. 奖励模型评分
+    with torch.no_grad():
+        reward_model_scores = []
+        for prompt, response in zip(prompts, responses):
+            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)
+            scale = 3.0
+            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 dapo_train_epoch(epoch, loader, iters, old_actor_model, ref_model,
+                     actor_scheduler, reward_model, reward_tokenizer,
+                     start_step=0, wandb=None):
+    actor_model.train()
+
+    for step, batch in enumerate(loader, start=start_step + 1):
+        prompts    = batch["prompt"]
+        batch_size = len(prompts)
+        G          = args.group_size
+
+        # 每个 prompt 重复 G 次，展开为 (batch_size * G) 条输入
+        prompts_expanded = [p for p in prompts for _ in range(G)]
+
+        enc = tokenizer(
+            prompts_expanded,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+            max_length=args.max_seq_len,
+            padding_side="left"
+        ).to(args.device)
+        prompt_length = enc.input_ids.shape[1]
+
+        with torch.no_grad():
+            model_for_gen = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+            gen_out = model_for_gen.generate(
+                input_ids=enc.input_ids,
+                attention_mask=enc.attention_mask,
+                max_new_tokens=args.max_gen_len,
+                do_sample=True,
+                temperature=0.8,
+                pad_token_id=tokenizer.pad_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+            )
+
+        # gen_out: (batch_size * G, seq_len)
+        responses_text = [
+            tokenizer.decode(gen_out[i, prompt_length:], skip_special_tokens=True)
+            for i in range(len(prompts_expanded))
+        ]
+        rewards_flat = calculate_rewards(prompts_expanded, responses_text, reward_model, reward_tokenizer, args)
+        # rewards_flat: (batch_size * G,)
+
+        # ------------------------------------------------------------------
+        # advantage = (r - mean_group) / std_group
+        # ------------------------------------------------------------------
+        rewards_grouped = rewards_flat.view(batch_size, G)
+        mean_r          = rewards_grouped.mean(dim=1, keepdim=True)
+        std_r           = rewards_grouped.std(dim=1, keepdim=True).clamp(min=1e-8)
+        advantages      = ((rewards_grouped - mean_r) / std_r).view(-1)  # (batch_size * G,)
+
+        # ------------------------------------------------------------------
+        # Dynamic Sampling
+        # ------------------------------------------------------------------
+        if args.use_dynamic_sampling == 1:
+            if (std_r < 1e-6).all().item():
+                if is_main_process():
+                    Logger(f"Step {step}: All groups have zero reward variance, skipping...")
+                continue
+
+        # ------------------------------------------------------------------
+        # Actor forward
+        # ------------------------------------------------------------------
+        full_mask = (gen_out != tokenizer.pad_token_id).long()
+
+        with autocast_ctx:
+            res      = actor_model(input_ids=gen_out, attention_mask=full_mask)
+            logits   = res.logits
+            aux_loss = res.aux_loss if lm_config.use_moe else torch.tensor(0.0, device=args.device)
+
+        labels      = gen_out[:, 1:].clone()
+        logp_tokens = F.log_softmax(logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
+
+        seq_len    = gen_out.size(1) - 1
+        resp_mask  = torch.arange(seq_len, device=gen_out.device).unsqueeze(0) >= prompt_length - 1
+        final_mask = resp_mask & (~labels.eq(tokenizer.pad_token_id))
+
+        # Token-level 平均 log-prob（防止长回复主导梯度）
+        token_count = final_mask.sum(dim=1).clamp(min=1).float()
+        actor_logp  = (logp_tokens * final_mask).sum(dim=1) / token_count
+
+        with torch.no_grad():
+            old_logits      = old_actor_model(input_ids=gen_out, attention_mask=full_mask).logits
+            old_logp_tokens = F.log_softmax(old_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
+            old_logp        = (old_logp_tokens * final_mask).sum(dim=1) / token_count
+
+            ref_logits      = ref_model(input_ids=gen_out, attention_mask=full_mask).logits
+            ref_logp_tokens = F.log_softmax(ref_logits[:, :-1], dim=-1).gather(2, labels.unsqueeze(-1)).squeeze(-1)
+            ref_logp        = (ref_logp_tokens * final_mask).sum(dim=1) / token_count
+
+        kl     = (actor_logp - old_logp).mean()
+        kl_ref = (actor_logp - ref_logp).mean()
+        ratio  = torch.exp(actor_logp - old_logp)
+
+        # ------------------------------------------------------------------
+        # Decoupled Clip
+        #   Adv > 0：只限上界，允许正向大步更新prompts_expanded = [p for p in prompts for _ in range(G)]
+        #   Adv ≤ 0：只限下界，防止负向更新反弹
+        # ------------------------------------------------------------------
+        surr1 = ratio * advantages
+        clipped_ratio = torch.where(
+            advantages > 0,
+            torch.clamp(ratio, max=1.0 + args.clip_epsilon_high),
+            torch.clamp(ratio, min=1.0 - args.clip_epsilon_low),
+        )
+        surr2       = clipped_ratio * advantages
+        policy_loss = -torch.min(surr1, surr2).mean()
+
+        loss = (policy_loss + args.kl_coef * kl_ref + aux_loss) / args.accumulation_steps
+        loss.backward()
+
+        if (step + 1) % args.accumulation_steps == 0:
+            clip_grad_norm_(actor_model.parameters(), args.grad_clip)
+            actor_optimizer.step()
+            actor_scheduler.step()
+            actor_optimizer.zero_grad()
+
+        if is_main_process():
+            response_ids = gen_out[:, enc.input_ids.shape[1]:]
+            is_eos       = (response_ids == tokenizer.eos_token_id)
+            eos_indices  = torch.argmax(is_eos.int(), dim=1)
+            lengths = torch.where(
+                is_eos.any(dim=1),
+                eos_indices + 1,
+                torch.tensor(response_ids.shape[1], device=is_eos.device)
+            )
+            avg_len    = lengths.float().mean()
+            reward_std = rewards_flat.std().item()
+
+            if wandb is not None:
+                wandb.log({
+                    "actor_loss":       policy_loss.item(),
+                    "reward":           rewards_flat.mean().item(),
+                    "reward_std":       reward_std,
+                    "kl":               kl.item(),
+                    "kl_ref":           kl_ref.item(),
+                    "avg_response_len": avg_len.item(),
+                    "actor_lr":         actor_optimizer.param_groups[0]['lr'],
+                })
+
+            Logger(
+                f"Epoch:[{epoch + 1}/{args.epochs}]({step}/{iters}), "
+                f"Loss: {policy_loss.item():.4f}, Reward: {rewards_flat.mean().item():.4f}, "
+                f"KL: {kl.item():.4f}, Len: {avg_len.item():.2f}"
+            )
+
+        if (step + 1) % args.update_old_actor_freq == 0:
+            raw_actor = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+            raw_actor = getattr(raw_actor, '_orig_mod', raw_actor)
+            old_actor_model.load_state_dict({k: v.detach().cpu() for k, v in raw_actor.state_dict().items()})
+            old_actor_model.to(args.device)
+
+        if (step % args.save_interval == 0 or step == iters - 1) and is_main_process():
+            actor_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'
+            raw_actor = actor_model.module if isinstance(actor_model, DistributedDataParallel) else actor_model
+            raw_actor = getattr(raw_actor, '_orig_mod', raw_actor)
+            torch.save({k: v.half().cpu() for k, v in raw_actor.state_dict().items()}, ckp)
+            lm_checkpoint(
+                lm_config, weight=args.save_weight, model=actor_model,
+                optimizer=actor_optimizer, epoch=epoch, step=step,
+                wandb=wandb, save_dir='../checkpoints', scheduler=actor_scheduler,
+            )
+            actor_model.train()
+
+        del enc, gen_out, responses_text, rewards_flat, rewards_grouped
+        del advantages, full_mask, logits, labels, logp_tokens, final_mask, token_count
+        del actor_logp, old_logits, old_logp, ref_logits, ref_logp
+        del kl, kl_ref, ratio, surr1, surr2, clipped_ratio, policy_loss, loss
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="MiniMind DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization)"
+    )
+    parser.add_argument("--save_dir",              type=str,   default="../out",                      help="模型保存目录")
+    parser.add_argument('--save_weight',            type=str,   default='dapo',                  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="prompt 级别的 batch size（实际前向 = batch_size * group_size）")
+    parser.add_argument("--learning_rate",         type=float, default=8e-8,                          help="Actor 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=8,                             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',            type=int,   default=512,                           help="隐藏层维度")
+    parser.add_argument('--num_hidden_layers',      type=int,   default=8,                             help="隐藏层数量")
+    parser.add_argument('--use_moe',                type=int,   default=0,  choices=[0, 1],            help="是否使用 MoE 架构（0=否，1=是）")
+    parser.add_argument('--max_seq_len',            type=int,   default=66,                            help="Prompt 最大长度")
+    parser.add_argument("--max_gen_len",            type=int,   default=1024,                          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")
+
+    # DAPO 核心参数
+    parser.add_argument("--clip_epsilon_low",      type=float, default=0.1,  help="Decoupled Clip 下界")
+    parser.add_argument("--clip_epsilon_high",     type=float, default=0.2,  help="Decoupled Clip 上界")
+    parser.add_argument("--group_size",            type=int,   default=8,    help="每个 prompt 的采样回复数")
+    parser.add_argument("--use_dynamic_sampling",  type=int,   default=1,   choices=[0, 1], help="是否开启动态采样过滤")
+    parser.add_argument("--length_penalty",        type=float, default=0.1,  help="长度惩罚系数")
+    parser.add_argument("--length_threshold",      type=int,   default=1200, help="触发长度惩罚的字符阈值")
+    parser.add_argument("--kl_coef",               type=float, default=0.02, help="KL 散度惩罚系数（相对 ref_model）")
+    parser.add_argument("--reasoning",             type=int,   default=1,   choices=[0, 1], help="推理模型类型（0=普通模型，1=推理模型）")
+    parser.add_argument("--update_old_actor_freq", type=int,   default=4,   help="更新 old_actor_model 的频率（步数）")
+    parser.add_argument("--reward_model_path",     type=str,   default="../../internlm2-1_8b-reward", help="Reward 模型路径")
+    parser.add_argument('--from_resume',            type=int,   default=0,   choices=[0, 1], help="是否自动检测并续训（0=否，1=是）")
+    parser.add_argument("--use_wandb",             action="store_true",                     help="是否使用 wandb/swanlab")
+    parser.add_argument("--wandb_project",         type=str,   default="MiniMind-DAPO",     help="wandb 项目名")
+    parser.add_argument("--use_compile",           type=int,   default=0,   choices=[0, 1], help="是否使用 torch.compile 加速（0=否，1=是）")
+    args = parser.parse_args()
+
+    # ========== 1. 初始化环境和随机种子 ==========
+    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))
+
+    # ========== 2. 配置目录、模型参数、检查 ckp ==========
+    os.makedirs(args.save_dir, exist_ok=True)
+    lm_config = MiniMindConfig(
+        hidden_size=args.hidden_size,
+        num_hidden_layers=args.num_hidden_layers,
+        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
+
+    # ========== 3. 设置混合精度 ==========
+    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)
+
+    # ========== 4. 配 wandb (SwanLab) ==========
+    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.init(
+            project=args.wandb_project,
+            name=f"MiniMind-DAPO-{args.learning_rate}",
+            id=wandb_id,
+            resume=resume,
+        )
+
+    # ========== 5. 初始化模型 ==========
+    base_weight = "reason" if args.reasoning == 1 else "full_sft"
+
+    actor_model, tokenizer = init_model(lm_config, base_weight, device=args.device)
+    if args.use_compile == 1:
+        actor_model = torch.compile(actor_model)
+
+    old_actor_model, _ = init_model(lm_config, base_weight, device=args.device)
+    old_actor_model = old_actor_model.eval().requires_grad_(False)
+
+    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)
+
+    # ========== 6. 数据、优化器、调度器 ==========
+    train_ds        = RLAIFDataset(args.data_path, tokenizer, max_length=(args.max_seq_len + args.max_gen_len))
+    train_sampler   = DistributedSampler(train_ds) if dist.is_initialized() else None
+    actor_optimizer = optim.AdamW(actor_model.parameters(), lr=args.learning_rate)
+
+    iters           = len(DataLoader(train_ds, batch_size=args.batch_size, sampler=train_sampler))
+    total_steps     = (iters // args.accumulation_steps) * args.epochs
+    actor_scheduler = CosineAnnealingLR(actor_optimizer, T_max=total_steps, eta_min=args.learning_rate / 10)
+
+    if ckp_data:
+        actor_model.load_state_dict(ckp_data['model'])
+        actor_optimizer.load_state_dict(ckp_data['optimizer'])
+        actor_scheduler.load_state_dict(ckp_data['scheduler'])
+        start_epoch = ckp_data['epoch']
+        start_step  = ckp_data.get('step', 0)
+    else:
+        start_epoch, start_step = 0, 0
+
+    if dist.is_initialized():
+        actor_model = DistributedDataParallel(actor_model, device_ids=[local_rank])
+        old_actor_model.to(args.device)
+
+    # ========== 7. 打印配置摘要 ==========
+    if is_main_process():
+        Logger("=" * 60)
+        Logger("DAPO Training Config (Critic-free):")
+        Logger(f"  group_size           = {args.group_size}")
+        Logger(f"  clip_epsilon_low     = {args.clip_epsilon_low}")
+        Logger(f"  clip_epsilon_high    = {args.clip_epsilon_high}")
+        Logger(f"  use_dynamic_sampling = {args.use_dynamic_sampling}")
+        Logger(f"  kl_coef              = {args.kl_coef}")
+        Logger(f"  effective_batch      = {args.batch_size} prompts × {args.group_size} = {args.batch_size * args.group_size}")
+        Logger("=" * 60)
+
+    # ========== 8. 开始训练 ==========
+    for epoch in range(start_epoch, args.epochs):
+        train_sampler and train_sampler.set_epoch(epoch)
+        setup_seed(42 + epoch)
+        indices = torch.randperm(len(train_ds)).tolist()
+        skip    = start_step if (epoch == start_epoch and start_step > 0) else 0
+        loader  = DataLoader(
+            train_ds,
+            batch_sampler=SkipBatchSampler(train_sampler or indices, args.batch_size, skip),
+            num_workers=args.num_workers,
+            pin_memory=True,
+        )
+        dapo_train_epoch(
+            epoch, loader, len(loader),
+            old_actor_model, ref_model,
+            actor_scheduler, reward_model, reward_tokenizer,
+            skip, wandb,
+        )
+
+    if dist.is_initialized():
+        dist.destroy_process_group()