[update] readme

README.md

@@ -1203,12 +1203,21 @@ $$
 **训练方式**：
 
 ```bash
+# ① 默认使用torch做rollout
 # 方式1
 torchrun --nproc_per_node N train_agent.py
 # 方式2
 python train_agent.py
 ```
 
+```bash
+# ② 使用sglang做rollout
+# 需先启动sglang server：
+python -m sglang.launch_server --model-path ./minimind-3 --attention-backend triton --host 0.0.0.0 --port 8998
+# 训练参数可参考：
+python train_agent.py --rollout_engine sglang --sglang_base_url http://localhost:8998 --sglang_shared_path ./ckpt_mm --data_path ../dataset/agent_rl_math.jsonl --use_wandb
+```
+
 > 训练后的模型权重文件默认每隔`save_interval步`保存为: `agent_*.pth`
 
 ![agent_rl_loss](./images/agent_rl_loss.jpg)

README_en.md

@@ -1202,12 +1202,21 @@ Here, tool call legality, `gt` hits, format closure, unfinished penalty, and Rew
 **Training method**:
 
 ```bash
+# ① Default: use torch for rollout
 # Method 1
 torchrun --nproc_per_node N train_agent.py
 # Method 2
 python train_agent.py
 ```
 
+```bash
+# ② Use sglang for rollout
+# Start sglang server first:
+python -m sglang.launch_server --model-path ./minimind-3 --attention-backend triton --host 0.0.0.0 --port 8998
+# Training parameters for reference:
+python train_agent.py --rollout_engine sglang --sglang_base_url http://localhost:8998 --sglang_shared_path ./ckpt_mm --data_path ../dataset/agent_rl_math.jsonl --use_wandb
+```
+
 > The trained model weight files are saved by default every `save_interval steps` as: `agent_*.pth`
 
 ![agent_rl_loss](./images/agent_rl_loss.jpg)