## Diagram: DeepSeek Model Training Flow ### Overview The image presents a diagram illustrating the training flow of DeepSeek models. It outlines different training paths and components, including models, prompts, training algorithms, and rewards. The diagram shows three distinct training pathways, each starting with a DeepSeek model and progressing through various stages of prompting, reasoning, and reward mechanisms. ### Components/Axes * **Models:** Represented by light purple rectangles. Examples include "DeepSeek V3 Base," "DeepSeek V3," "DeepSeek R1 Zero," "DeepSeek R1 Dev-1," "DeepSeek R1 Dev-2," "DeepSeek R1 Dev-3," and "DeepSeek R1." * **Prompts+Responses:** Represented by light gray rectangles. Examples include "Reasoning," "Non-Reasoning," and "Cold Start Long CoT." * **Training Algorithms:** Represented by dark blue rectangles. Examples include "RL" (Reinforcement Learning) and "SFT" (Supervised Fine-Tuning). * **Prompts:** Represented by light blue rectangles. Examples include "Reasoning Prompts," "Diverse Prompts," and "Filter Accuracy & Format, Refine DeepSeek V3+Human." * **Rewards:** Represented by dark gray rectangles. Examples include "Rule-based Reward & Lang. Consistency" and "Rule-based Reward & Preference Reward." * **Post-Processing:** Represented by dark gray rectangles. * **Arrows:** Indicate the flow of data and processes between components. * **Sampling:** Indicates a branching point where data is sampled. **Legend (Located on the right side of the diagram):** * Models: Light Purple * Prompts+Responses: Light Gray * Training Algorithms: Dark Blue * Prompts: Light Blue * Rewards: Dark Gray * Post-Processing: Dark Gray ### Detailed Analysis **Pathway 1 (Leftmost):** 1. Starts with "DeepSeek V3 Base" (light purple). 2. Goes through "RL" (Reinforcement Learning - dark blue) applied to "Reasoning Prompts" and "Accuracy & Format" (light blue). 3. Proceeds to "DeepSeek R1 Zero" (light purple). 4. "Sampling" occurs. 5. The sampled data is processed through "Reasoning Prompts" which includes "Filter Accuracy & Format" and "Refine DeepSeek V3+Human" (light blue). **Pathway 2 (Middle):** 1. Starts with "DeepSeek V3 Base" (light purple). 2. Goes through "SFT" (Supervised Fine-Tuning - dark blue) applied to "Cold Start Long CoT" (light gray). 3. Proceeds to "DeepSeek R1 Dev-1" (light purple). 4. Goes through "RL" (Reinforcement Learning - dark blue) applied to "Reasoning Prompts" and "Rule-based Reward & Lang. Consistency" (light blue). 5. Proceeds to "DeepSeek R1 Dev-2" (light purple). 6. A feedback loop connects "DeepSeek R1 Dev-2" back to the "Reasoning Prompts" stage of Pathway 1. **Pathway 3 (Rightmost):** 1. Starts with "DeepSeek V3" (light purple) and "DeepSeek V3 Base" (light purple). 2. Both pathways are "Sampling". 3. Goes through "SFT" (Supervised Fine-Tuning - dark blue) applied to "Non-Reasoning" and "Reasoning" (light gray). 4. Proceeds to "DeepSeek R1 Dev-3" (light purple). 5. Goes through "RL" (Reinforcement Learning - dark blue) applied to "Diverse Prompts" and "Rule-based Reward & Preference Reward" (light blue). 6. Proceeds to "DeepSeek R1" (light purple). ### Key Observations * The diagram illustrates three distinct training pathways for DeepSeek models. * Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT) are key training algorithms used. * The models progress through stages of prompting, reasoning, and reward mechanisms. * Sampling is used to branch the training process. * There is a feedback loop from "DeepSeek R1 Dev-2" to the "Reasoning Prompts" stage of Pathway 1. ### Interpretation The diagram provides a high-level overview of the training process for DeepSeek models. It highlights the use of different training algorithms, prompting strategies, and reward mechanisms to optimize model performance. The presence of multiple pathways and sampling suggests that different training approaches are being explored and compared. The feedback loop indicates an iterative refinement process where the model's performance is used to adjust the training process. The diagram suggests a complex and multifaceted approach to training DeepSeek models, incorporating both supervised and reinforcement learning techniques.

\n ## Diagram: DeepSeek Model Development Flow ### Overview The image depicts a flowchart illustrating the development process of the DeepSeek model, progressing through various stages of Reinforcement Learning (RL), Supervised Fine-Tuning (SFT), and sampling techniques. The diagram highlights the iterative refinement of the model through different prompt strategies, reward systems, and post-processing steps. ### Components/Axes The diagram consists of rectangular nodes representing model versions or stages, connected by arrows indicating the flow of development. A legend on the right side categorizes the nodes by color: * **Purple:** Models * **Pink:** Prompts+Responses * **Blue:** Training Algorithms * **Orange:** Prompts * **Light Blue:** Rewards * **Gray:** Post-Processing The diagram's flow starts from the top and branches out, converging at certain points. Key model names include "DeepSeek V3 Base", "DeepSeek R1 Zero", "DeepSeek R1 Dev-1", "DeepSeek R1 Dev-2", "DeepSeek R1 Dev-3", and "DeepSeek R1". ### Detailed Analysis or Content Details The diagram can be broken down into four main branches originating from "DeepSeek V3 Base": **Branch 1 (Leftmost):** 1. "DeepSeek V3 Base" -> RL (Training Algorithm) with "Reasoning Prompts" (Prompts) focusing on "Accuracy & Format" (Rewards). 2. This leads to "DeepSeek R1 Zero". 3. "DeepSeek R1 Zero" -> Sampling -> "Reasoning Prompts" (Prompts) for "Filter, Accuracy & Format" (Post-Processing) and "Refine DeepSeek V3+Human". **Branch 2 (Second from Left):** 1. "DeepSeek V3 Base" -> SFT (Training Algorithm) with "Cold Start Long CoT" (Prompts). 2. This leads to "DeepSeek R1 Dev-1". 3. "DeepSeek R1 Dev-1" -> RL (Training Algorithm) with "Reasoning Prompts" (Prompts) and "Rule-based Reward & Lang. Consistency" (Rewards). 4. This leads to "DeepSeek R1 Dev-2". **Branch 3 (Second from Right):** 1. "DeepSeek V3 Base" -> SFT (Training Algorithm) with "Non-Reasoning" (Prompts) and "Reasoning" (Prompts). 2. This leads to "DeepSeek R1 Dev-3". 3. "DeepSeek R1 Dev-3" -> RL (Training Algorithm) with "Diverse Prompts" (Prompts) and "Rule-based Reward & Preference Reward" (Rewards). 4. This leads to "DeepSeek R1". **Branch 4 (Rightmost):** 1. "DeepSeek V3 Base" -> Sampling. This branch does not lead to any further model development stages. The "Sampling" steps are visually represented as arrows connecting different stages. ### Key Observations * The model development process is highly iterative, with multiple feedback loops involving RL and SFT. * Different prompt strategies ("Reasoning Prompts", "Cold Start Long CoT", "Non-Reasoning", "Diverse Prompts") are employed at various stages. * Reward systems evolve from focusing on "Accuracy & Format" to incorporating "Lang. Consistency" and "Preference Reward". * The diagram suggests a parallel development approach, with multiple model versions ("Dev-1", "Dev-2", "Dev-3") being refined simultaneously. * The rightmost branch, starting with "DeepSeek V3 Base" and leading to "Sampling", appears to be a separate exploratory path. ### Interpretation The diagram illustrates a sophisticated model development pipeline for DeepSeek, emphasizing the importance of iterative refinement through RL and SFT. The use of diverse prompt strategies and reward systems suggests a focus on improving both the accuracy and the quality of the model's responses. The parallel development of multiple model versions indicates a commitment to exploring different approaches and identifying the most effective configurations. The "Sampling" branch might represent a process of generating data or evaluating model performance. The diagram highlights a complex interplay between model architecture, training algorithms, prompt engineering, and reward design, all aimed at creating a robust and high-performing language model. The diagram does not provide quantitative data, but rather a qualitative overview of the development process. It is a high-level representation of the workflow, and further details would be needed to understand the specific parameters and configurations used at each stage.

## Diagram: DeepSeek Model Training Pipeline ### Overview The image is a technical flowchart illustrating the training and refinement pipeline for the DeepSeek series of AI models. It depicts a multi-stage process starting from base models, applying various training algorithms (Reinforcement Learning - RL, Supervised Fine-Tuning - SFT), using different prompt types, and resulting in progressively refined model versions. The flow is primarily from left to right and top to bottom, with branching and merging paths. ### Components/Axes The diagram is composed of interconnected boxes and arrows, color-coded according to a legend on the right side. **Legend (Right Side):** * **Models (Light Purple):** Represents the AI model at various stages. * **Prompts+Responses (Light Gray):** Represents the data used for training. * **Training Algorithms (Dark Blue):** Represents the core training methods (RL, SFT). * **Prompts (Light Blue):** Represents the input prompts used. * **Rewards (Medium Blue):** Represents the reward signals used in RL. * **Post-Processing (Dark Gray):** Represents filtering and refinement steps. **Spatial Layout:** * The pipeline begins at the top-left with "DeepSeek V3 Base". * The legend is positioned vertically along the right edge of the diagram. * The flow splits into three main vertical columns or branches that eventually converge. ### Detailed Analysis The pipeline can be segmented into three primary processing branches: **1. Left Branch (Initial RL & Refinement):** * **Start:** `DeepSeek V3 Base` (Model). * **Step 1:** Applies `RL` (Training Algorithm) using `Reasoning Prompts` (Prompt) focused on `Accuracy & Format` (Reward). * **Output:** `DeepSeek R1 Zero` (Model). * **Step 2:** `Sampling` (Process) from `DeepSeek R1 Zero` generates `Reasoning Prompts` (Prompt). * **Step 3:** These prompts undergo `Post-Processing` via `Filter` (for `Accuracy & Format`) and `Refine` (using `DeepSeek V3+Human` input). * **Connection:** The output of this refinement feeds into the middle branch. **2. Middle Branch (Cold Start & Iterative RL):** * **Start:** `DeepSeek V3 Base` (Model). * **Step 1:** Applies `SFT` (Training Algorithm) using `Cold Start Long CoT` (Prompt+Response). * **Output:** `DeepSeek R1 Dev-1` (Model). * **Step 2:** Applies `RL` (Training Algorithm) using `Reasoning Prompts` (Prompt) with `Rule-based Reward & Lang. Consistency` (Reward). * **Output:** `DeepSeek R1 Dev-2` (Model). * **Connection:** This branch receives input from the Left Branch's post-processing step and its output feeds into the Right Branch. **3. Right Branch (Final SFT & RL):** * **Start:** `DeepSeek V3 Base` (Model) and `DeepSeek V3` (Model, from an external source indicated by an arrow). * **Step 1:** Applies `SFT` (Training Algorithm) using a combination of `Non-Reasoning` and `Reasoning` (Prompt+Response) data, which is generated via `Sampling`. * **Output:** `DeepSeek R1 Dev-3` (Model). * **Step 2:** Applies `RL` (Training Algorithm) using `Diverse Prompts` (Prompt) with `Rule-based Reward & Preference Reward` (Reward). * **Final Output:** `DeepSeek R1` (Model). ### Key Observations * **Iterative Refinement:** The process is highly iterative, with models (`R1 Zero`, `R1 Dev-1`, `R1 Dev-2`, `R1 Dev-3`) serving as intermediate checkpoints before the final `DeepSeek R1`. * **Hybrid Training:** The pipeline combines Supervised Fine-Tuning (SFT) with Reinforcement Learning (RL) at multiple stages. * **Prompt Diversity:** Training uses a variety of prompt types: `Reasoning Prompts`, `Cold Start Long CoT`, `Non-Reasoning`, and `Diverse Prompts`. * **Complex Reward Systems:** RL stages employ different reward mechanisms, evolving from simple `Accuracy & Format` to more complex `Rule-based Reward & Lang. Consistency` and finally `Rule-based Reward & Preference Reward`. * **Human & Model Integration:** The `Refine` step in the left branch explicitly incorporates both `DeepSeek V3` model output and `Human` input. * **Sampling as a Connector:** `Sampling` is used as a key process to generate training data from intermediate models. ### Interpretation This diagram details a sophisticated, multi-path training curriculum designed to create the `DeepSeek R1` model. It suggests a philosophy of starting with a strong base (`V3 Base`), creating an initial reasoning-focused model (`R1 Zero`), and then using that model's outputs to "cold start" a more capable development model (`R1 Dev-1`). Parallel tracks likely explore different training methodologies (e.g., pure reasoning vs. mixed reasoning/non-reasoning SFT). The convergence of branches into the final `R1 Dev-3` and then `DeepSeek R1` indicates an ensemble or best-of-breed approach, where insights and data products from different experimental paths are combined. The progression of reward functions from format-focused to preference-focused implies a training strategy that first instills basic structural competence and then refines the model's outputs to align with human preferences or other quality metrics. The entire pipeline emphasizes iterative improvement, data generation from intermediate models, and the strategic combination of supervised and reinforcement learning techniques.

## Flowchart: DeepSeek Model Development Pipeline ### Overview The image depicts a multi-stage development pipeline for DeepSeek language models, showing iterative refinement processes from base models to specialized versions. The flowchart uses color-coded components to represent different stages and elements of the development process. ### Components/Axes **Legend (right side):** - Purple: Models (DeepSeek V3 Base, R1 Zero, R1 Dev-1, R1 Dev-2, R1) - Gray: Prompts+Responses - Blue: Training Algorithms (RL, SFT) - Light Blue: Prompts - Dark Blue: Rewards - Dark Gray: Post-Processing **Key Elements:** 1. **Models** (Purple boxes): - DeepSeek V3 Base (appears 4x) - DeepSeek R1 Zero - DeepSeek R1 Dev-1 - DeepSeek R1 Dev-2 - DeepSeek R1 2. **Training Algorithms** (Blue boxes): - RL (Reinforcement Learning) - SFT (Supervised Fine-Tuning) 3. **Prompts** (Light Blue boxes): - Reasoning Prompts - Diverse Prompts - Rule-based Reward & Lang. Consistency - Rule-based Reward & Preference Reward 4. **Processes** (Gray boxes): - Sampling - Filter - Refine - Cold Start Long CoT - Non-Reasoning Reasoning ### Detailed Analysis **Flow Structure:** 1. **Left Branch (Accuracy & Format Focus):** - DeepSeek V3 Base → RL (Reasoning Prompts) → DeepSeek R1 Zero - Sampling → Filter → Refine → DeepSeek V3 + Human - Final output: Refined Reasoning Prompts 2. **Center Branch (Cold Start Long CoT):** - DeepSeek V3 Base → SFT → Cold Start Long CoT - DeepSeek R1 Dev-1 → RL (Rule-based Reward & Lang. Consistency) - Output: DeepSeek R1 Dev-2 3. **Right Branch (Diverse Prompts):** - DeepSeek V3 Base → SFT → Non-Reasoning Reasoning - DeepSeek R1 Dev-3 → RL (Rule-based Reward & Preference Reward) - Output: DeepSeek R1 **Spatial Grounding:** - Legend positioned on the right side - Main flowchart divided into three vertical sections - Model versions arranged in descending order from top to bottom - Training algorithms (RL/SFT) positioned between model versions - Prompts/rewards located in lower sections **Textual Elements:** - All model names in purple boxes - Training algorithms in blue boxes - Prompts in light blue boxes - Rewards in dark blue boxes - Processes in gray boxes ### Key Observations 1. Iterative refinement process from base model (V3) to specialized versions (R1) 2. Dual training approaches: RL for reasoning capabilities and SFT for foundational learning 3. Progressive complexity in prompts and rewards across development stages 4. Explicit separation between reasoning and non-reasoning pathways 5. Human-in-the-loop component in the left branch (V3 + Human) ### Interpretation This pipeline demonstrates a systematic approach to developing advanced language models through: 1. **Progressive Specialization:** Starting with general capabilities (V3 Base) and refining through multiple development stages (R1 Zero → R1 Dev → R1) 2. **Hybrid Training:** Combining supervised learning (SFT) with reinforcement learning (RL) to balance breadth and depth of knowledge 3. **Quality Control:** Multiple filtering/refinement steps and human evaluation components 4. **Performance Optimization:** Use of rule-based rewards for language consistency and preference alignment The flowchart suggests a research-driven development methodology focused on enhancing reasoning capabilities while maintaining linguistic consistency and human alignment. The separation of reasoning and non-reasoning pathways indicates an intentional design choice to optimize different aspects of model performance.