diff --git a/model/model_memory.py b/model/model_memory.py
index 975e1f8..a4c474f 100644
--- a/model/model_memory.py
+++ b/model/model_memory.py
@@ -337,7 +337,7 @@ class MiniMindBlock(nn.Module):
         self.memory_gate = MemoryGate(config)
         self.gated_memory_fusion = GatedMemoryFusion(config)
 
-        self.attentionpool = nn.Linear(config.dim, 1)
+        # self.attentionpool = nn.Linear(config.dim, 1)
 
     def forward(self, x, pos_cis, memory_bank, tok_embeddings, collect_ema_stats=False):
         """
diff --git a/run_file/experiment_1_4_7-06.sh b/run_file/experiment_1_4_7-06.sh
new file mode 100644
index 0000000..962212e
--- /dev/null
+++ b/run_file/experiment_1_4_7-06.sh
@@ -0,0 +1,249 @@
+#!/bin/bash
+
+#########################################################
+# 实验1.4.7 - Memory Bank文本初始化 + 部分冻结机制
+# 
+# 实验目标:
+# 1. 验证使用有意义文本进行memory_bank初始化的效果
+# 2. 验证部分memory_bank冻结机制(freeze_ratio=0.4)的效果
+#
+# 关键特性:
+# - 使用sentence_trex_data.json文本数据初始化memory_bank
+# - 冻结20%的memory_bank条目，保护重要知识
+# - Token-based memory机制 + EMA更新
+# - Product Key Memory架构
+#########################################################
+
+echo "=========================================="
+echo "🚀 开始实验 1.4.7 - Memory Bank优化"
+echo "🔥 新特性: 文本初始化 + 部分冻结机制"
+echo "=========================================="
+
+# 实验配置
+EXPERIMENT_NAME="experiment_1_4_7-06"
+OUTPUT_DIR="out/${EXPERIMENT_NAME}"
+LOG_FILE="${OUTPUT_DIR}/experiment.log"
+PID_FILE="${OUTPUT_DIR}/train.pid"
+
+# 创建输出目录
+mkdir -p $OUTPUT_DIR
+
+echo "📂 实验输出目录: $OUTPUT_DIR"
+echo "📝 日志文件: $LOG_FILE"
+
+# 核心参数配置
+MODEL_TYPE="model_memory"                    # 🔥 使用memory架构
+DIM=512
+N_LAYERS=8
+N_HEADS=32
+MAX_SEQ_LEN=512
+
+# 🔥 Memory Bank配置 - 实验1.4.7关键参数
+KNOWLEDGE_NUM=1048576                        # 1M条记忆（2^20）
+KNOWLEDGE_LENGTH=8                          # 每条记忆32个token
+KNOWLEDGE_DIM=128                           # 记忆向量维度128
+FREEZE_RATIO=0.6                            # 🔥 新特性: 冻结60%的记忆条目
+
+# EMA更新配置
+USE_EMA_UPDATE="True"
+EMA_DECAY=0.9                               # EMA衰减率
+EMA_UPDATE_FREQ=5                           # EMA更新频率
+
+# 训练配置
+EPOCHS=3
+BATCH_SIZE=48
+ACCUMULATION_STEPS=8
+LEARNING_RATE=2e-4
+DTYPE="bfloat16"
+GRAD_CLIP=1.0
+BALANCE_LOSS_COEF=0.01                      # 平衡损失系数
+
+# 数据路径配置
+DATA_PATH="/home/zym/Code/stable/merged_pretrain.jsonl"
+DATABASE_INIT_PATH="/home/zym/Code/stable/sentence_trex_data.json"  # 🔥 文本数据初始化
+CACHE_PATH="cache/memory_bank_init_${KNOWLEDGE_NUM}_${KNOWLEDGE_LENGTH}.pt"           # 🔥 Memory初始化缓存
+
+# GPU和性能配置
+export CUDA_VISIBLE_DEVICES=0
+NUM_WORKERS=8
+MIXED_PRECISION="bf16"
+
+# 监控配置
+USE_SWANLAB="True"
+SWANLAB_PROJECT="MiniMind-Experiment-1.4.7"
+SWANLAB_ONLINE="False"                      # 离线模式
+
+# 验证和日志配置
+LOG_INTERVAL=100
+VAL_INTERVAL=200
+PROFILE="True"
+PROFILE_INTERVAL=10
+MEMORY_MONITOR="False"                      # 关闭内存监控降低开销
+
+echo "=========================================="
+echo "📋 实验配置摘要"
+echo "=========================================="
+echo "🔥 核心特性:"
+echo "  - Model Type: $MODEL_TYPE"
+echo "  - Memory Bank Size: $KNOWLEDGE_NUM 条"
+echo "  - Memory Length: $KNOWLEDGE_LENGTH tokens"
+echo "  - Freeze Ratio: $FREEZE_RATIO (冻结 $((KNOWLEDGE_NUM * 20 / 100)) 条记忆)"
+echo "  - Text Initialization: $DATABASE_INIT_PATH"
+echo ""
+echo "🏗️ 模型架构:"
+echo "  - Dimension: $DIM"
+echo "  - Layers: $N_LAYERS" 
+echo "  - Heads: $N_HEADS"
+echo "  - Max Seq Len: $MAX_SEQ_LEN"
+echo ""
+echo "📚 训练设置:"
+echo "  - Epochs: $EPOCHS"
+echo "  - Batch Size: $BATCH_SIZE"
+echo "  - Learning Rate: $LEARNING_RATE"
+echo "  - Data Type: $DTYPE"
+echo ""
+echo "⚡ EMA配置:"
+echo "  - EMA Decay: $EMA_DECAY"
+echo "  - Update Frequency: $EMA_UPDATE_FREQ"
+echo ""
+echo "📊 监控:"
+echo "  - SwanLab Project: $SWANLAB_PROJECT"
+echo "  - Log Interval: $LOG_INTERVAL"
+echo "=========================================="
+
+# 检查必要文件
+echo "🔍 检查必要文件..."
+if [[ ! -f "$DATA_PATH" ]]; then
+    echo "❌ 错误: 训练数据文件不存在: $DATA_PATH"
+    exit 1
+fi
+
+if [[ ! -f "$DATABASE_INIT_PATH" ]]; then
+    echo "❌ 错误: Memory初始化数据文件不存在: $DATABASE_INIT_PATH"
+    exit 1
+fi
+
+echo "✅ 文件检查通过"
+
+# 构建训练命令 - 参考experiment_1_4_6.sh的成功模式
+TRAIN_CMD="CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python train_pretrain_accelerate.py"
+TRAIN_CMD+=" --out_dir \"$OUTPUT_DIR\""
+TRAIN_CMD+=" --epochs $EPOCHS"
+TRAIN_CMD+=" --embedding_epoch 2"
+TRAIN_CMD+=" --batch_size $BATCH_SIZE"
+TRAIN_CMD+=" --learning_rate $LEARNING_RATE"
+TRAIN_CMD+=" --dtype $DTYPE"
+TRAIN_CMD+=" --num_workers $NUM_WORKERS"
+TRAIN_CMD+=" --accumulation_steps $ACCUMULATION_STEPS"
+TRAIN_CMD+=" --grad_clip $GRAD_CLIP"
+TRAIN_CMD+=" --warmup_iters 0"
+TRAIN_CMD+=" --log_interval $LOG_INTERVAL"
+TRAIN_CMD+=" --val_interval $VAL_INTERVAL"
+TRAIN_CMD+=" --dim $DIM"
+TRAIN_CMD+=" --n_layers $N_LAYERS"
+TRAIN_CMD+=" --n_heads $N_HEADS"
+TRAIN_CMD+=" --max_seq_len $MAX_SEQ_LEN"
+TRAIN_CMD+=" --data_path \"$DATA_PATH\""
+TRAIN_CMD+=" --knowledge_num $KNOWLEDGE_NUM"
+TRAIN_CMD+=" --knowledge_length $KNOWLEDGE_LENGTH"
+TRAIN_CMD+=" --knowledge_dim $KNOWLEDGE_DIM"
+TRAIN_CMD+=" --database_init_path \"$DATABASE_INIT_PATH\""
+TRAIN_CMD+=" --cluster_cache_path \"$CACHE_PATH\""
+TRAIN_CMD+=" --model_type \"$MODEL_TYPE\""
+TRAIN_CMD+=" --balance_loss_coef $BALANCE_LOSS_COEF"
+
+# 添加可选的flag参数（不需要值的参数）
+TRAIN_CMD+=" --use_swanlab"
+TRAIN_CMD+=" --profile"
+TRAIN_CMD+=" --use_flash_attn"
+
+# 添加有值的可选参数
+TRAIN_CMD+=" --swanlab_project \"$SWANLAB_PROJECT\""
+# TRAIN_CMD+=" --swanlab_online $SWANLAB_ONLINE"
+TRAIN_CMD+=" --profile_interval $PROFILE_INTERVAL"
+
+# 添加memory monitor参数（如果启用）
+if [[ "$MEMORY_MONITOR" == "True" ]]; then
+    TRAIN_CMD+=" --memory_monitor"
+fi
+
+echo ""
+echo "🚀 启动训练..."
+echo "📝 完整训练命令:"
+echo "$TRAIN_CMD"
+echo ""
+echo "⏰ 预计训练时间: 约6-8小时"
+echo "📊 实时监控: 查看 $LOG_FILE"
+echo ""
+
+# 记录命令到日志文件
+echo "执行命令: $TRAIN_CMD" >> "$LOG_FILE"
+echo "开始时间: $(date)" >> "$LOG_FILE"
+
+# 创建训练脚本（参考1.4.6的成功模式）
+TRAIN_SCRIPT="/tmp/train_1_4_7-06.sh"
+cat > "$TRAIN_SCRIPT" << EOF
+#!/bin/bash
+cd /home/zym/Code/Minimind
+source /home/user/miniconda3/bin/activate
+conda activate minimind
+$TRAIN_CMD
+echo "结束时间: \$(date)"
+echo "退出代码: \$?"
+EOF
+chmod +x "$TRAIN_SCRIPT"
+
+# 使用nohup后台运行训练脚本
+nohup bash "$TRAIN_SCRIPT" >> "$LOG_FILE" 2>&1 &
+TRAIN_PID=$!
+echo $TRAIN_PID > $PID_FILE
+
+echo "=========================================="
+echo "✅ 实验1.4.7已启动"
+echo "🆔 进程ID: $TRAIN_PID"
+echo "📝 日志文件: $LOG_FILE"
+echo "📊 监控命令: tail -f $LOG_FILE"
+echo "🛑 停止命令: kill $TRAIN_PID"
+echo "=========================================="
+echo ""
+echo "🔥 实验1.4.7 - Memory Bank优化特性:"
+echo "  ✨ 文本数据初始化 (sentence_trex_data.json)"
+echo "  ✨ 部分冻结机制 (freeze_ratio=0.4)"
+echo "  ✨ Token-based EMA更新"
+echo "  ✨ Product Key Memory架构"
+echo ""
+echo "📋 监控要点:"
+echo "  - 初始化阶段：观察文本数据加载和缓存"
+echo "  - 训练阶段：关注frozen_memories统计"
+echo "  - EMA更新：监控update_ratio和coverage指标"
+echo "  - 生成质量：对比词组连贯性改善"
+echo ""
+echo "⚡ 进程状态检查:"
+echo "ps aux | grep $TRAIN_PID"
+echo ""
+
+# 显示初始进程状态
+sleep 2
+if ps -p $TRAIN_PID > /dev/null; then
+    echo "✅ 训练进程正在运行 (PID: $TRAIN_PID)"
+    
+    # 显示前几行日志
+    echo ""
+    echo "📋 初始日志预览:"
+    echo "----------------------------------------"
+    timeout 5 tail -f $LOG_FILE | head -10 || echo "日志文件尚未生成，请稍等..."
+    echo "----------------------------------------"
+else
+    echo "❌ 训练进程启动失败，请检查日志:"
+    echo "cat $LOG_FILE"
+fi
+
+echo ""
+echo "🎯 实验1.4.7核心验证点:"
+echo "  1. Memory bank是否成功用文本数据初始化"
+echo "  2. 冻结机制是否正常工作 (20%条目不更新)"
+echo "  3. 生成质量是否有明显改善"
+echo "  4. 训练稳定性是否提升"
+echo ""
+echo "📖 实验记录: experiment/EXPERIMENT_1_4_7-06.md"
+echo "🚀 实验1.4.7启动完成！"
\ No newline at end of file
diff --git a/run_file/experiment_1_4_9-02.sh b/run_file/experiment_1_4_9-02.sh
index b5603d5..e36ca9e 100644
--- a/run_file/experiment_1_4_9-02.sh
+++ b/run_file/experiment_1_4_9-02.sh
@@ -59,8 +59,8 @@ GRAD_CLIP=1.0
 BALANCE_LOSS_COEF=0.01                      # 平衡损失系数
 
 # 数据路径配置
-DATA_PATH="./dataset/stable/merged_pretrain.jsonl"
-DATABASE_INIT_PATH="./dataset/stable/sentence_trex_data.json"  # 🔥 文本数据初始化
+DATA_PATH="/home/zym/Code/stable/merged_pretrain.jsonl"
+DATABASE_INIT_PATH="/home/zym/Code/stable/sentence_trex_data.json"  # 🔥 文本数据初始化
 CACHE_PATH="cache/memory_bank_init_${KNOWLEDGE_NUM}_${KNOWLEDGE_LENGTH}.pt"           # 🔥 Memory初始化缓存
 
 # GPU和性能配置
@@ -243,4 +243,4 @@ echo "  3. 生成质量是否有明显改善"
 echo "  4. 训练稳定性是否提升"
 echo ""
 echo "📖 实验记录: experiment/EXPERIMENT_1_4_9-02.md"
-echo "🚀 实验1.4.9-02启动完成！"
\ No newline at end of file
+echo "🚀 实验1.4.9-02启动完成！"
diff --git a/run_file/experiment_1_4_9-04.sh b/run_file/experiment_1_4_9-04.sh
new file mode 100644
index 0000000..bd1b1c4
--- /dev/null
+++ b/run_file/experiment_1_4_9-04.sh
@@ -0,0 +1,246 @@
+#!/bin/bash
+
+#########################################################
+# 实验1.4.9-04 - Memory Bank文本初始化 + 部分冻结机制
+# 
+# 实验目标:
+# 1. 验证使用有意义文本进行memory_bank初始化的效果
+# 2. 验证部分memory_bank冻结机制(freeze_ratio=0.2)的效果
+#
+# 关键特性:
+# - 使用sentence_trex_data.json文本数据初始化memory_bank
+# - 冻结20%的memory_bank条目，保护重要知识
+# - Token-based memory机制 + EMA更新
+# - Product Key Memory架构
+#########################################################
+
+echo "=========================================="
+echo "🚀 开始实验 1.4.9-04 - Memory Bank优化"
+echo "🔥 新特性: 文本初始化 + 部分冻结机制"
+echo "=========================================="
+
+# 实验配置
+EXPERIMENT_NAME="experiment_1_4_9-04"
+OUTPUT_DIR="out/${EXPERIMENT_NAME}"
+LOG_FILE="${OUTPUT_DIR}/experiment.log"
+PID_FILE="${OUTPUT_DIR}/train.pid"
+
+# 创建输出目录
+mkdir -p $OUTPUT_DIR
+
+echo "📂 实验输出目录: $OUTPUT_DIR"
+echo "📝 日志文件: $LOG_FILE"
+
+# 核心参数配置
+MODEL_TYPE="model_memory"                    # 🔥 使用memory架构
+DIM=512
+N_LAYERS=1
+N_HEADS=32
+MAX_SEQ_LEN=512
+
+# 🔥 Memory Bank配置 - 实验1.4.9-04关键参数
+KNOWLEDGE_NUM=1048576                        # 1M条记忆（2^20）
+KNOWLEDGE_LENGTH=8                          # 每条记忆32个token
+KNOWLEDGE_DIM=128                           # 记忆向量维度128
+FREEZE_RATIO=0.2                            # 🔥 新特性: 冻结20%的记忆条目
+
+# EMA更新配置
+USE_EMA_UPDATE="True"
+EMA_DECAY=0.9                               # EMA衰减率
+EMA_UPDATE_FREQ=5                           # EMA更新频率
+
+# 训练配置
+EPOCHS=3
+BATCH_SIZE=48
+ACCUMULATION_STEPS=8
+LEARNING_RATE=2e-4
+DTYPE="bfloat16"
+GRAD_CLIP=1.0
+BALANCE_LOSS_COEF=0.01                      # 平衡损失系数
+
+# 数据路径配置
+DATA_PATH="/home/zym/Code/stable/merged_pretrain.jsonl"
+DATABASE_INIT_PATH="/home/zym/Code/stable/sentence_trex_data.json"  # 🔥 文本数据初始化
+CACHE_PATH="cache/memory_bank_init_${KNOWLEDGE_NUM}_${KNOWLEDGE_LENGTH}.pt"           # 🔥 Memory初始化缓存
+
+# GPU和性能配置
+export CUDA_VISIBLE_DEVICES=0
+NUM_WORKERS=8
+MIXED_PRECISION="bf16"
+
+# 监控配置
+USE_SWANLAB="True"
+SWANLAB_PROJECT="MiniMind-Experiment-1.4.9-04"
+SWANLAB_ONLINE="False"                      # 离线模式
+
+# 验证和日志配置
+LOG_INTERVAL=100
+VAL_INTERVAL=200
+PROFILE="True"
+PROFILE_INTERVAL=10
+MEMORY_MONITOR="False"                      # 关闭内存监控降低开销
+
+echo "=========================================="
+echo "📋 实验配置摘要"
+echo "=========================================="
+echo "🔥 核心特性:"
+echo "  - Model Type: $MODEL_TYPE"
+echo "  - Memory Bank Size: $KNOWLEDGE_NUM 条"
+echo "  - Memory Length: $KNOWLEDGE_LENGTH tokens"
+echo "  - Freeze Ratio: $FREEZE_RATIO (冻结 $((KNOWLEDGE_NUM * 20 / 100)) 条记忆)"
+echo "  - Text Initialization: $DATABASE_INIT_PATH"
+echo ""
+echo "🏗️ 模型架构:"
+echo "  - Dimension: $DIM"
+echo "  - Layers: $N_LAYERS" 
+echo "  - Heads: $N_HEADS"
+echo "  - Max Seq Len: $MAX_SEQ_LEN"
+echo ""
+echo "📚 训练设置:"
+echo "  - Epochs: $EPOCHS"
+echo "  - Batch Size: $BATCH_SIZE"
+echo "  - Learning Rate: $LEARNING_RATE"
+echo "  - Data Type: $DTYPE"
+echo ""
+echo "⚡ EMA配置:"
+echo "  - EMA Decay: $EMA_DECAY"
+echo "  - Update Frequency: $EMA_UPDATE_FREQ"
+echo ""
+echo "📊 监控:"
+echo "  - SwanLab Project: $SWANLAB_PROJECT"
+echo "  - Log Interval: $LOG_INTERVAL"
+echo "=========================================="
+
+# 检查必要文件
+echo "🔍 检查必要文件..."
+if [[ ! -f "$DATA_PATH" ]]; then
+    echo "❌ 错误: 训练数据文件不存在: $DATA_PATH"
+    exit 1
+fi
+
+if [[ ! -f "$DATABASE_INIT_PATH" ]]; then
+    echo "❌ 错误: Memory初始化数据文件不存在: $DATABASE_INIT_PATH"
+    exit 1
+fi
+
+echo "✅ 文件检查通过"
+
+# 构建训练命令 - 参考experiment_1_4_6.sh的成功模式
+TRAIN_CMD="CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES python train_pretrain_accelerate.py"
+TRAIN_CMD+=" --out_dir \"$OUTPUT_DIR\""
+TRAIN_CMD+=" --epochs $EPOCHS"
+TRAIN_CMD+=" --embedding_epoch 2"
+TRAIN_CMD+=" --batch_size $BATCH_SIZE"
+TRAIN_CMD+=" --learning_rate $LEARNING_RATE"
+TRAIN_CMD+=" --dtype $DTYPE"
+TRAIN_CMD+=" --num_workers $NUM_WORKERS"
+TRAIN_CMD+=" --accumulation_steps $ACCUMULATION_STEPS"
+TRAIN_CMD+=" --grad_clip $GRAD_CLIP"
+TRAIN_CMD+=" --warmup_iters 0"
+TRAIN_CMD+=" --log_interval $LOG_INTERVAL"
+TRAIN_CMD+=" --val_interval $VAL_INTERVAL"
+TRAIN_CMD+=" --dim $DIM"
+TRAIN_CMD+=" --n_layers $N_LAYERS"
+TRAIN_CMD+=" --n_heads $N_HEADS"
+TRAIN_CMD+=" --max_seq_len $MAX_SEQ_LEN"
+TRAIN_CMD+=" --data_path \"$DATA_PATH\""
+TRAIN_CMD+=" --knowledge_num $KNOWLEDGE_NUM"
+TRAIN_CMD+=" --knowledge_length $KNOWLEDGE_LENGTH"
+TRAIN_CMD+=" --knowledge_dim $KNOWLEDGE_DIM"
+TRAIN_CMD+=" --database_init_path \"$DATABASE_INIT_PATH\""
+TRAIN_CMD+=" --cluster_cache_path \"$CACHE_PATH\""
+TRAIN_CMD+=" --model_type \"$MODEL_TYPE\""
+TRAIN_CMD+=" --balance_loss_coef $BALANCE_LOSS_COEF"
+
+# 添加可选的flag参数（不需要值的参数）
+# TRAIN_CMD+=" --use_swanlab"
+TRAIN_CMD+=" --profile"
+TRAIN_CMD+=" --use_flash_attn"
+
+# 添加有值的可选参数
+TRAIN_CMD+=" --swanlab_project \"$SWANLAB_PROJECT\""
+TRAIN_CMD+=" --swanlab_online $SWANLAB_ONLINE"
+TRAIN_CMD+=" --profile_interval $PROFILE_INTERVAL"
+
+# 添加memory monitor参数（如果启用）
+if [[ "$MEMORY_MONITOR" == "True" ]]; then
+    TRAIN_CMD+=" --memory_monitor"
+fi
+
+echo ""
+echo "🚀 启动训练..."
+echo "📝 完整训练命令:"
+echo "$TRAIN_CMD"
+echo ""
+echo "⏰ 预计训练时间: 约6-8小时"
+echo "📊 实时监控: 查看 $LOG_FILE"
+echo ""
+
+# 记录命令到日志文件
+echo "执行命令: $TRAIN_CMD" >> "$LOG_FILE"
+echo "开始时间: $(date)" >> "$LOG_FILE"
+
+# 创建训练脚本（参考1.4.6的成功模式）
+TRAIN_SCRIPT="/tmp/train_1_4_9-04.sh"
+cat > "$TRAIN_SCRIPT" << EOF
+#!/bin/bash
+$TRAIN_CMD
+echo "结束时间: \$(date)"
+echo "退出代码: \$?"
+EOF
+chmod +x "$TRAIN_SCRIPT"
+
+# 使用nohup后台运行训练脚本
+nohup bash "$TRAIN_SCRIPT" >> "$LOG_FILE" 2>&1 &
+TRAIN_PID=$!
+echo $TRAIN_PID > $PID_FILE
+
+echo "=========================================="
+echo "✅ 实验1.4.9-04已启动"
+echo "🆔 进程ID: $TRAIN_PID"
+echo "📝 日志文件: $LOG_FILE"
+echo "📊 监控命令: tail -f $LOG_FILE"
+echo "🛑 停止命令: kill $TRAIN_PID"
+echo "=========================================="
+echo ""
+echo "🔥 实验1.4.9-04 - Memory Bank优化特性:"
+echo "  ✨ 文本数据初始化 (sentence_trex_data.json)"
+echo "  ✨ 部分冻结机制 (freeze_ratio=0.2)"
+echo "  ✨ Token-based EMA更新"
+echo "  ✨ Product Key Memory架构"
+echo ""
+echo "📋 监控要点:"
+echo "  - 初始化阶段：观察文本数据加载和缓存"
+echo "  - 训练阶段：关注frozen_memories统计"
+echo "  - EMA更新：监控update_ratio和coverage指标"
+echo "  - 生成质量：对比词组连贯性改善"
+echo ""
+echo "⚡ 进程状态检查:"
+echo "ps aux | grep $TRAIN_PID"
+echo ""
+
+# 显示初始进程状态
+sleep 2
+if ps -p $TRAIN_PID > /dev/null; then
+    echo "✅ 训练进程正在运行 (PID: $TRAIN_PID)"
+    
+    # 显示前几行日志
+    echo ""
+    echo "📋 初始日志预览:"
+    echo "----------------------------------------"
+    timeout 5 tail -f $LOG_FILE | head -10 || echo "日志文件尚未生成，请稍等..."
+    echo "----------------------------------------"
+else
+    echo "❌ 训练进程启动失败，请检查日志:"
+    echo "cat $LOG_FILE"
+fi
+
+echo ""
+echo "🎯 实验1.4.9-04核心验证点:"
+echo "  1. Memory bank是否成功用文本数据初始化"
+echo "  2. 冻结机制是否正常工作 (20%条目不更新)"
+echo "  3. 生成质量是否有明显改善"
+echo "  4. 训练稳定性是否提升"
+echo ""
+echo "📖 实验记录: experiment/EXPERIMENT_1_4_9-04.md"
+echo "🚀 实验1.4.9-04启动完成！"
diff --git a/train_pretrain_accelerate.py b/train_pretrain_accelerate.py
index 35c6d65..e622af7 100644
--- a/train_pretrain_accelerate.py
+++ b/train_pretrain_accelerate.py
@@ -1356,7 +1356,7 @@ def main():
     parser.add_argument("--model_size", type=float, default=50.0, help="模型大小")
     parser.add_argument("--swanlab_online", type=bool, default=False, help="是否使用在线SwanLab服务")
     parser.add_argument("--balance_loss_coef", type=float, default=0.01, help="平衡损失系数")
-    parser.add_argument("--val_data_path", type=str, default="dataset/stable/eval_data.json", help="验证数据集路径")
+    parser.add_argument("--val_data_path", type=str, default="/home/zym/Code/stable/eval_data.json", help="验证数据集路径")
     parser.add_argument("--val_interval", type=int, default=100, help="验证评估间隔")
     args = parser.parse_args()