diff --git a/README.md b/README.md
index ca04e75..eaf4e18 100644
--- a/README.md
+++ b/README.md
@@ -101,7 +101,7 @@
 | 模型 | 参数量 | Release |
 |------|--------|---------|
 | minimind-3 | 64M | 2026.04.01 |
-| minimind-3-moe | 198M / A64M | 2026.04.01 |
+| minimind-3-moe | 198M-A64M | 2026.04.01 |
 | minimind2-small | 26M | 2025.04.26 |
 | minimind2-moe | 145M | 2025.04.26 |
 | minimind2 | 104M | 2025.04.26 |
@@ -118,7 +118,7 @@
 <summary> <b>🔥 2026-04-01</b> </summary>
 
  - 发布 `minimind-3` / `minimind-3-moe`：结构、Tokenizer、训练链路、推理接口与默认配置全面更新
-- 结构主线对齐 `Qwen3 / Qwen3-MoE` 生态：Dense 约 `64M`，MoE 约 `198M / A64M`，并移除了 shared expert 设计
+- 结构主线对齐 `Qwen3 / Qwen3-MoE` 生态：Dense 约 `64M`，MoE 约 `198M-A64M`，并移除了 shared expert 设计
 - 默认训练数据切换为 `pretrain_t2t(_mini).jsonl`、`sft_t2t(_mini).jsonl`、`rlaif.jsonl`、`agent_rl.jsonl` 与 `agent_rl_math.jsonl`
 - 移除独立 `train_reason.py`；思考能力统一由 `chat_template + <think>` 与 `open_thinking` 自适应开关控制
 - `toolcall` 能力已混入 `sft_t2t / sft_t2t_mini` 主线数据，默认 `full_sft` 即具备基础 Tool Call 能力；同时新增 `scripts/chat_api.py` 等推理示例
@@ -575,7 +575,7 @@ MiniMind训练数据集下载地址： [ModelScope](https://www.modelscope.cn/da
 | Model Name | params | len_vocab | max_pos | rope_theta | n_layers | d_model | kv_heads | q_heads | note |
 |------------|--------|-----------|---------|------------|----------|---------|----------|---------|------|
 | minimind-3 | 64M | 6400 | 32768 | 1e6 | 8 | 768 | 4 | 8 | Dense |
-| minimind-3-moe | 198M / A64M | 6400 | 32768 | 1e6 | 8 | 768 | 4 | 8 | 4 experts / top-1 |
+| minimind-3-moe | 198M-A64M | 6400 | 32768 | 1e6 | 8 | 768 | 4 | 8 | 4 experts / top-1 |
 | minimind2-small | 26M | 6400 | 32768 | 1e6 | 8 | 512 | 2 | 8 | 历史版本 |
 | minimind2-moe | 145M | 6400 | 32768 | 1e6 | 8 | 640 | 2 | 8 | 历史版本 |
 | minimind2 | 104M | 6400 | 32768 | 1e6 | 16 | 768 | 2 | 8 | 历史版本 |
@@ -621,7 +621,7 @@ MobileLLM 的一个核心观察是：在参数量固定时，深度往往比宽
 | Model Name | params | pretrain_t2t_mini | sft_t2t_mini | toolcall | RLAIF |
 |------------|--------|-------------------|--------------|----------|-------|
 | minimind-3 | 64M | ≈1.21h<br/>≈1.57￥ | ≈1.10h<br/>≈1.43￥ | ≈0.9h<br/>≈1.17￥ | ≈1.1h<br/>≈1.43￥ |
-| minimind-3-moe | 198M / A64M | ≈1.69h<br/>≈2.20￥ | ≈1.54h<br/>≈2.00￥ | ≈1.26h<br/>≈1.64￥ | ≈1.54h<br/>≈2.00￥ |
+| minimind-3-moe | 198M-A64M | ≈1.69h<br/>≈2.20￥ | ≈1.54h<br/>≈2.00￥ | ≈1.26h<br/>≈1.64￥ | ≈1.54h<br/>≈2.00￥ |
 
 ---
 
diff --git a/README_en.md b/README_en.md
index a3d1535..784a303 100644
--- a/README_en.md
+++ b/README_en.md
@@ -101,7 +101,7 @@ Meanwhile, third-party large model frameworks and tool libraries, such as `trans
 | Model | Parameters | Release |
 |------|--------|---------|
 | minimind-3 | 64M | 2026.04.01 |
-| minimind-3-moe | 198M / A64M | 2026.04.01 |
+| minimind-3-moe | 198M-A64M | 2026.04.01 |
 | minimind2-small | 26M | 2025.04.26 |
 | minimind2-moe | 145M | 2025.04.26 |
 | minimind2 | 104M | 2025.04.26 |
@@ -117,7 +117,7 @@ Meanwhile, third-party large model frameworks and tool libraries, such as `trans
 <summary> <b>🔥 2026-04-01</b> </summary>
 
  - Released `minimind-3` / `minimind-3-moe`: comprehensive updates to structure, Tokenizer, training pipeline, inference interface, and default configuration
-- Main branch structure aligned with `Qwen3 / Qwen3-MoE` ecosystem: Dense approximately `64M`, MoE approximately `198M / A64M`, and removed shared expert design
+- Main branch structure aligned with `Qwen3 / Qwen3-MoE` ecosystem: Dense approximately `64M`, MoE approximately `198M-A64M`, and removed shared expert design
 - Default training data switched to `pretrain_t2t(_mini).jsonl`, `sft_t2t(_mini).jsonl`, `rlaif.jsonl`, `agent_rl.jsonl`, and `agent_rl_math.jsonl`
 - Removed standalone `train_reason.py`; thinking capability is now unified through `chat_template + <think>` and `open_thinking` adaptive switch control
 - `toolcall` capability has been merged into `sft_t2t / sft_t2t_mini` main branch data, default `full_sft` already has basic Tool Call capability; also added inference examples such as `scripts/chat_api.py`
@@ -574,7 +574,7 @@ To modify model configuration, see [./model/model_minimind.py](./model/model_min
 | Model Name | params | len_vocab | max_pos | rope_theta | n_layers | d_model | kv_heads | q_heads | note |
 |------------|--------|-----------|---------|------------|----------|---------|----------|---------|------|
 | minimind-3 | 64M | 6400 | 32768 | 1e6 | 8 | 768 | 4 | 8 | Dense |
-| minimind-3-moe | 198M / A64M | 6400 | 32768 | 1e6 | 8 | 768 | 4 | 8 | 4 experts / top-1 |
+| minimind-3-moe | 198M-A64M | 6400 | 32768 | 1e6 | 8 | 768 | 4 | 8 | 4 experts / top-1 |
 | minimind2-small | 26M | 6400 | 32768 | 1e6 | 8 | 512 | 2 | 8 | Historical version |
 | minimind2-moe | 145M | 6400 | 32768 | 1e6 | 8 | 640 | 2 | 8 | Historical version |
 | minimind2 | 104M | 6400 | 32768 | 1e6 | 16 | 768 | 2 | 8 | Historical version |
@@ -620,7 +620,7 @@ For reference, GPT-3's parameter settings are as follows:
 | Model Name | params | pretrain_t2t_mini | sft_t2t_mini | toolcall | RLAIF |
 |------------|--------|-------------------|--------------|----------|-------|
 | minimind-3 | 64M | ≈1.21h<br/>≈1.57￥ | ≈1.10h<br/>≈1.43￥ | ≈0.9h<br/>≈1.17￥ | ≈1.1h<br/>≈1.43￥ |
-| minimind-3-moe | 198M / A64M | ≈1.69h<br/>≈2.20￥ | ≈1.54h<br/>≈2.00￥ | ≈1.26h<br/>≈1.64￥ | ≈1.54h<br/>≈2.00￥ |
+| minimind-3-moe | 198M-A64M | ≈1.69h<br/>≈2.20￥ | ≈1.54h<br/>≈2.00￥ | ≈1.26h<br/>≈1.64￥ | ≈1.54h<br/>≈2.00￥ |
 
 ---