## Diagram: SFT and RL Process Flow
### Overview
The image is a diagram illustrating a sequential process involving Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). The process is divided into four stages: Cold Start SFT, Overall SFT, Distillation, and RL. Each stage is represented by a rounded rectangle containing a title and a brief description. Arrows connect the stages, indicating the flow of the process.
### Components/Axes
* **Stages:** The diagram consists of four stages, each represented by a rounded rectangle.
* **Arrows:** Arrows indicate the flow of the process from one stage to the next.
* **Titles:** Each stage has a title indicating the type of process involved.
* **Descriptions:** Each stage has a brief description providing more details about the process.
### Detailed Analysis
* **Stage 1: Cold Start SFT**
* Title: Cold Start SFT
* Description: Foundational Reasoning Skills (Math/Code/STEM)
* **Stage 2: Overall SFT**
* Title: Overall SFT
* Description: General/Curriculum Learning (General Conversation/Agent/Reasoning Curriculum Data)
* **Stage 3: Distillation**
* Title: Distillation
* Description: Dual-Level Preference Distillation (Large Model -> Small Model)
* **Stage 4: RL**
* Title: RL
* Description: Multi-Stage RL With Robust Reward System (STEM/Code/Human Preference Alignment)
### Key Observations
* The process starts with Cold Start SFT, focusing on foundational reasoning skills.
* It then moves to Overall SFT, which involves general and curriculum learning.
* Distillation is used to transfer knowledge from a large model to a smaller model.
* The final stage is RL, which uses a multi-stage approach with a robust reward system.
### Interpretation
The diagram illustrates a process for training AI models, starting with supervised fine-tuning to establish foundational skills and then moving to more general learning. Distillation is used to create smaller, more efficient models, and reinforcement learning is used to optimize the model's performance. The process emphasizes the importance of both supervised and reinforcement learning techniques in developing AI models. The progression suggests a structured approach to model training, starting with basic skills and gradually increasing complexity.
