[doc] fix: update install instruction and retool readme (volcengine#3824)

### What does this PR do?

Update docs according to user feedback:
1. add a note: dockerfiles provide more detailed installation steps than
installation doc
2. add a "how it works" section in retool readme to avoid training
without SFT.

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Author: chenhaiq

docs/start/install.rst

@@ -123,6 +123,10 @@ Install from custom environment
 
 We recommend to use docker images for convenience. However, if your environment is not compatible with the docker image, you can also install verl in a python environment.
 
+.. note::
+
+    - Dockerfile provides more details than this installation instructions. You can find examples in each Dockerfile, for example `verl0.6-cu128-torch2.8.0-fa2.7.4 Dockerfile.base <https://github.com/volcengine/verl/blob/v0.6.0/docker/verl0.6-cu128-torch2.8.0-fa2.7.4/Dockerfile.base>`_ .
+
 
 Pre-requisites
 ::::::::::::::
@@ -133,7 +137,8 @@ so we put them in the :ref:`Post-installation` step.
 
 .. note::
 
-    The installation steps below are recommended configurations for the latest version of verl.
+    - The installation steps below are recommended configurations for the latest version of verl.
+
     If you are trying to customize your own environment, please ignore the strict constraints.
 
 We need to install the following pre-requisites:

recipe/retool/README.md

@@ -7,6 +7,18 @@
 - RL dataset: [BytedTsinghua-SIA/DAPO-Math-17k](https://huggingface.co/datasets/BytedTsinghua-SIA/DAPO-Math-17k)
 - Val dataset: [yentinglin/aime_2025](https://huggingface.co/datasets/yentinglin/aime_2025)
 
+## How it works
+
+ Retool's workflow is divided into two key phases:
+
+1.  Cold Start and Supervised Fine Tuning (SFT)
+    
+     The data generation pipeline builds a high-quality dataset containing code-enhanced inference trajectories, and supervised fine-tuning enables the model to master basic Tool call (e.g., code execution) and analysis of the execution results.
+    
+2.  Dynamic Interaction and Policy Optimization (RL).
+    
+     With the verl Reinforcement Learning framework, the model dynamically inserts code blocks during inference and interacts with the sandbox environment in real-time, generating a hybrid trajectory of natural language thinking and code snippets, sending the code to the sandbox for asynchronous execution when code termination markers are detected, and the execution results (success outputs/errors) are fed back to the model for guiding the subsequent inference. This "think-execute-feedback" cycle, together with the design of rewards based on the accuracy of the final answer, enables the model to independently optimize the Tool call strategy, and improves the reasoning efficiency and computational accuracy.
+
 ## SFT
 1. Data preparation
 ```bash