diff --git a/metagpt/ext/sela/README.md b/metagpt/ext/sela/README.md
index 829306e36..a942fdb7d 100644
--- a/metagpt/ext/sela/README.md
+++ b/metagpt/ext/sela/README.md
@@ -1,29 +1,26 @@
 # SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
 
-
-
 ## 1. Data Preparation
 
-- Download Datasets：https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink
-- Download and prepare datasets from scratch:
-```
-cd data
-python dataset.py --save_analysis_pool
-python hf_data.py --save_analysis_pool
-```
+You can either download the datasets from the link or prepare the datasets from scratch.
+- **Download Datasets:** [Dataset Link](https://deepwisdom.feishu.cn/drive/folder/RVyofv9cvlvtxKdddt2cyn3BnTc?from=from_copylink)
+- **Download and prepare datasets from scratch:**
+    ```bash
+    cd data
+    python dataset.py --save_analysis_pool
+    python hf_data.py --save_analysis_pool
+    ```
 
-## 2. Configs
+## 2. Configurations
 
 ### Data Config
 
-`datasets.yaml` Provide base prompts, metrics, target columns for respective datasets
-
-- Modify `datasets_dir` to the root directory of all the datasets in `data.yaml`
-
+- **`datasets.yaml`:** Provide base prompts, metrics, and target columns for respective datasets.
+- **`data.yaml`:** Modify `datasets_dir` to the base directory of all prepared datasets.
 
 ### LLM Config
 
-```
+```yaml
 llm:
   api_type: 'openai'
   model: deepseek-coder
@@ -32,237 +29,57 @@ ### LLM Config
   temperature: 0.5
 ```
 
-### Budget
-Experiment rollouts k = 5, 10, 20
-
-
-### Prompt Usage
-
-- Use the function `generate_task_requirement` in `dataset.py` to get task requirement.
-  - If the method is non-DI-based, set `is_di=False`.
-  - Use `utils.DATA_CONFIG` as `data_config`
-
 
 ## 3. SELA
 
 ### Run SELA
 
 #### Setup
-In the root directory, 
 
-```
+```bash
 pip install -e .
 
-cd expo
+cd metagpt/ext/sela
 
 pip install -r requirements.txt
 ```
 
-#### Run
+#### Running Experiments
 
-- Examples
-  ```
-  python run_experiment.py --exp_mode mcts --task titanic --rollouts 10
-  python run_experiment.py --exp_mode mcts --task house-prices --rollouts 10 --low_is_better
-  ```
-
-
-- `--rollouts` - The number of rollouts
-
-- `--use_fixed_insights` - In addition to the generated insights, include the fixed insights saved in `expo/insights/fixed_insights.json`
-  
-- `--low_is_better` - If the dataset has reg metric, remember to use `--low_is_better`
-
-- `--from_scratch` - Do not use pre-processed insight pool, generate new insight pool based on dataset before running MCTS, facilitating subsequent tuning to propose search space prompts
-
-- `--role_timeout` - The timeout for the role
-  - This feature limits the duration of a single simulation, making the experiment duration more controllable (for example, if you do ten rollouts and set role_timeout to 1,000, the experiment will stop at the latest after 10,000s)
-
-
-- `--max_depth` - The maximum depth of MCTS, default is 4 (nodes at this depth directly return the previous simulation result without further expansion)
-
-- `--load_tree` - If MCTS was interrupted due to certain reasons but had already run multiple rollouts, you can use `--load_tree`.
-  - For example:
-    ```
+- **Examples:**
+    ```bash
     python run_experiment.py --exp_mode mcts --task titanic --rollouts 10
-    ```
-  - If this was interrupted after running three rollouts, you can use `--load_tree`:
-    ```
-    python run_experiment.py --exp_mode mcts --task titanic --rollouts 7 --load_tree
+    python run_experiment.py --exp_mode mcts --task house-prices --rollouts 10 --low_is_better
     ```
 
+#### Parameters
 
-#### Ablation Study
+- **`--rollouts`:** The number of rollouts.
+- **`--use_fixed_insights`:** Include fixed insights saved in `expo/insights/fixed_insights.json`.
+- **`--low_is_better`:** Use this if the dataset has a regression metric.
+- **`--from_scratch`:** Generate a new insight pool based on the dataset before running MCTS.
+- **`--role_timeout`:** Limits the duration of a single simulation (e.g., `10 rollouts with timeout 1,000` = max 10,000s).
+- **`--max_depth`:** Set the maximum depth of MCTS (default is 4).
+- **`--load_tree`:** Load an existing MCTS tree if the previous experiment was interrupted.
+    - Example:
+      ```bash
+      python run_experiment.py --exp_mode mcts --task titanic --rollouts 10
+      ```
+    - To resume:
+      ```bash
+      python run_experiment.py --exp_mode mcts --task titanic --rollouts 7 --load_tree
+      ```
 
-**DI RandomSearch**
+### Ablation Study
 
-- Single insight
-`python run_experiment.py --exp_mode rs --task titanic --rs_mode single`
+**RandomSearch**
 
-- Set insight
-`python run_experiment.py --exp_mode rs --task titanic --rs_mode set`
+- **Use a single insight:**
+    ```bash
+    python run_experiment.py --exp_mode rs --task titanic --rs_mode single
+    ```
 
-
-## 4. Evaluation
-
-Each baseline needs to produce `dev_predictions.csv`和`test_predictions.csv`. Each csv file only needs a `target` column.
-
-- Use the function `evaluate_score` to evaluate.
-
-#### MLE-Bench
-**Note: mle-bench requires python 3.11 or higher**
-```
-git clone https://github.com/openai/mle-bench.git
-cd mle-bench
-pip install -e .
-```
-
-```
-mlebench prepare -c <competition-id> --data-dir <dataset-dir-save-path>
-```
-
-Enter the following command to run the experiment:
-```
-python run_experiment.py --exp_mode mcts --custom_dataset_dir <dataset-dir-save-path/prepared/public> --rollouts 10 --from_scratch --role_timeout 3600
-```
-
-
-## 5. Baselines
-
-### AIDE
-
-#### Setup
-The version of AIDE we use is dated September 30, 2024
-```
-git clone https://github.com/WecoAI/aideml.git
-git checkout 77953247ea0a5dc1bd502dd10939dd6d7fdcc5cc
-```
-
-Modify `aideml/aide/utils/config.yaml` - change `k_fold_validation`, `code model`, and `feedback model` as follows:
-
-```yaml
-# agent hyperparams
-agent:
-  # how many improvement iterations to run
-  steps: 10
-  # whether to instruct the agent to use CV (set to 1 to disable)
-  k_fold_validation: 1
-  # LLM settings for coding
-  code:
-    model: deepseek-coder
-    temp: 0.5
-
-  # LLM settings for evaluating program output / tracebacks
-  feedback:
-    model: deepseek-coder
-    temp: 0.5
-
-  # hyperparameters for the tree search
-  search:
-    max_debug_depth: 3
-    debug_prob: 0.5
-    num_drafts: 5
-```
-
-Since Deepseek is compatible to OpenAI's API, change `base_url` into `your own url`，`api_key` into `your api key`
-
-```
-export OPENAI_API_KEY="your api key"
-export OPENAI_BASE_URL="your own url"
-```
-
-Modify `aideml/aide/backend/__init__.py`'s line 30 and below:
-
-```python
-model_kwargs = model_kwargs | {
-        "model": model,
-        "temperature": temperature,
-        "max_tokens": max_tokens,
-    }
-if "claude-" in model:
-  query_func = backend_anthropic.query
-else:
-  query_func = backend_openai.query
-```
-
-Since deepseekV2.5 no longer supports system message using function call, modify `aideml/aide/agent.py`'s line 312:
-
-```python
-response = cast(
-    dict,
-    query(
-        system_message=None,
-        user_message=prompt,
-        func_spec=review_func_spec,
-        model=self.acfg.feedback.model,
-        temperature=self.acfg.feedback.temp,
-    ),
-)
-```
-
-Modify and install:
-
-```
-cd aideml
-pip install -e .
-```
-
-#### Run
-
-Run the following script to get the running results, a `log` folder and a `workspace` folder will be generated in the current directory
-The `log` folder will contain the experimental configuration and the generated scheme, and the `workspace` folder will save the final results generated by aide
-
-```
-python runner/aide.py
-```
-
-### Autogluon
-#### Setup
-```
-pip install -U pip
-pip install -U setuptools wheel
-pip install autogluon==1.1.1
-```
-
-For Tabular data:
-```
-python run_expriment.py --exp_mode autogluon --task {task_name}
-```
-For Multimodal data:
-```
-python run_expriment.py --exp_mode autogluon --task {task_name} --is_multimodal
-```
-Replace {task_name} with the specific task you want to run.
-
-
-### AutoSklearn
-#### System requirements
-auto-sklearn has the following system requirements:
-
-- Linux operating system (for example Ubuntu)
-
-- Python (>=3.7)
-
-- C++ compiler (with C++11 supports)
-
-In case you try to install Auto-sklearn on a system where no wheel files for the pyrfr package are provided (see here for available wheels) you also need:
-
-- SWIG [(get SWIG here).](https://www.swig.org/survey.html)
-
-For an explanation of missing Microsoft Windows and macOS support please check the Section [Windows/macOS compatibility](https://automl.github.io/auto-sklearn/master/installation.html#windows-macos-compatibility).
-
-#### Setup
-```
-pip install auto-sklearn==0.15.0
-```
-
-#### Run
-```
-python run_experiment.py --exp_mode autosklearn --task titanic
-```
-
-### Base DI 
-For setup, check 4.
-- `python run_experiment.py --exp_mode base --task titanic --num_experiments 10`
-- Specifically instruct DI to use AutoGluon: `--special_instruction ag`
-- Specifically instruct DI to use the stacking ensemble method: `--special_instruction stacking`
\ No newline at end of file
+- **Use a set of insights:**
+    ```bash
+    python run_experiment.py --exp_mode rs --task titanic --rs_mode set
+    ```
\ No newline at end of file
diff --git a/metagpt/ext/sela/runner/README.md b/metagpt/ext/sela/runner/README.md
new file mode 100644
index 000000000..7c031f1ee
--- /dev/null
+++ b/metagpt/ext/sela/runner/README.md
@@ -0,0 +1,198 @@
+# SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
+
+This document provides instructions for running baseline models. To start with, ensure that you prepare the datasets as instructed in `sela/README.md`.
+
+## Baselines
+
+### 1. AIDE
+
+#### Setup
+
+We use the AIDE version from September 30, 2024. Clone the repository and check out the specified commit:
+
+```bash
+git clone https://github.com/WecoAI/aideml.git
+git checkout 77953247ea0a5dc1bd502dd10939dd6d7fdcc5cc
+```
+
+
+Modify `aideml/aide/utils/config.yaml` to set the following parameters:
+
+```yaml
+# agent hyperparams
+agent:
+  steps: 10  # Number of improvement iterations
+  k_fold_validation: 1  # Set to 1 to disable cross-validation
+  code:
+    model: deepseek-coder
+    temp: 0.5
+  feedback:
+    model: deepseek-coder
+    temp: 0.5
+  search:
+    max_debug_depth: 3
+    debug_prob: 0.5
+    num_drafts: 5
+```
+
+Update your OpenAI API credentials in the environment:
+
+```bash
+export OPENAI_API_KEY="your api key"
+export OPENAI_BASE_URL="your own url"
+```
+
+Modify `aideml/aide/backend/__init__.py` (line 30 and below):
+
+```python
+model_kwargs = model_kwargs | {
+        "model": model,
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+    }
+if "claude-" in model:
+  query_func = backend_anthropic.query
+else:
+  query_func = backend_openai.query
+```
+
+Since Deepseek V2.5 no longer supports system messages using function calls, modify `aideml/aide/agent.py` (line 312):
+
+```python
+response = cast(
+    dict,
+    query(
+        system_message=None,
+        user_message=prompt,
+        func_spec=review_func_spec,
+        model=self.acfg.feedback.model,
+        temperature=self.acfg.feedback.temp,
+    ),
+)
+```
+
+Finally, install AIDE:
+
+```bash
+cd aideml
+pip install -e .
+```
+
+#### Run
+
+Execute the following script to generate results. A `log` folder (containing experimental configurations) and a `workspace` folder (storing final results) will be created:
+
+```bash
+python runner/aide.py
+```
+
+---
+
+### 2. Autogluon
+
+#### Setup
+
+Install Autogluon:
+
+```bash
+pip install -U pip
+pip install -U setuptools wheel
+pip install autogluon==1.1.1
+```
+
+#### Run
+
+For Tabular data:
+
+```bash
+python run_experiment.py --exp_mode autogluon --task {task_name}
+```
+
+For Multimodal data:
+
+```bash
+python run_experiment.py --exp_mode autogluon --task {task_name} --is_multimodal
+```
+
+Replace `{task_name}` with the specific task you want to run.
+
+---
+
+### 3. AutoSklearn
+
+**Note:**
+AutoSklearn requires:
+- Linux operating system (e.g., Ubuntu)
+- Python (>=3.7)
+- C++ compiler (with C++11 support)
+
+If installing on a system without wheel files for the `pyrfr` package, you also need:
+
+- [SWIG](https://www.swig.org/survey.html)
+
+Refer to the [Windows/macOS compatibility](https://automl.github.io/auto-sklearn/master/installation.html#windows-macos-compatibility) section for further details.
+
+#### Setup
+
+Install AutoSklearn:
+
+```bash
+pip install auto-sklearn==0.15.0
+```
+
+#### Run
+
+Execute the following command for the Titanic task:
+
+```bash
+python run_experiment.py --exp_mode autosklearn --task titanic
+```
+
+---
+
+### 4. Base Data Interpreter
+
+Run the following command for the Titanic task:
+
+```bash
+python run_experiment.py --exp_mode base --task titanic --num_experiments 10
+```
+
+---
+
+### 5. Custom Baselines
+
+To run additional baselines:
+
+- Each baseline must produce `dev_predictions.csv` and `test_predictions.csv` with a `target` column.
+- Use the `evaluate_score` function for evaluation.
+
+---
+
+## MLE-Bench
+
+**Note:** MLE-Bench requires Python 3.11 or higher.
+
+#### Setup
+
+Clone the repository and install:
+
+```bash
+git clone https://github.com/openai/mle-bench.git
+cd mle-bench
+pip install -e .
+```
+
+Prepare the data:
+
+```bash
+mlebench prepare -c <competition-id> --data-dir <dataset-dir-save-path>
+```
+
+#### Run the MLE-Bench Experiment
+
+Run the following command to execute the experiment:
+
+```bash
+python run_experiment.py --exp_mode mcts --custom_dataset_dir <dataset-dir-save-path/prepared/public> --rollouts 10 --from_scratch --role_timeout 3600
+```
\ No newline at end of file