Introduction

AI reasoning models have made incredible progress, with leading advancements in both proprietary and open-source research. One of the most exciting recent developments is DeepSeek-R1, an open-source model that brings advanced reasoning capabilities to the community.

What sets DeepSeek-R1 apart is its multi-stage training pipeline, which integrates:
Cold-Start Fine-Tuning for structured reasoning
Reinforcement Learning (RL) to enhance problem-solving in math, logic, and coding
Supervised Fine-Tuning & Distillation for improved efficiency and accessibility

This blog explores DeepSeek-R1’s training process step by step, highlighting how it achieves strong reasoning abilities while remaining open-source and scalable. Let’s dive in!

The DeepSeek-R1 Training Pipeline

Here’s a visual breakdown of DeepSeek-R1’s training process, which explains the whole process in one flashcard.

Step 1: Cold-Start Fine-Tuning

DeepSeek-V3 Base model underwent Cold-Start Fine-Tuning to establish a strong foundation in reasoning and structured responses. This phase ensured the model could generate clear, step-by-step logical explanations before reinforcement learning (RL) optimization.

The model was fine-tuned on thousands of high-quality reasoning samples, including:

• Few-shot model outputs with reflection & verification

  • Used existing models to generate long Chain-of-Thought (CoT) responses, followed by self-reflection to refine answers.

• Post-processed outputs of DeepSeek-R1-Zero

  • DeepSeek-R1-Zero was an earlier experimental version trained via RL without fine-tuning, but it struggled with readability and coherence.

  • Selected well-structured outputs were post-processed and included in fine-tuning.

• Human-refined responses

  • Human annotators improved clarity, coherence, and correctness in generated responses.

  • Focused on making outputs easier to read while maintaining logical depth.

Why Was Cold-Start Fine-Tuning Necessary?

• Prevented instability in early-stage RL training (ensured model started with structured reasoning).

• Allowed the model to learn stepwise CoT reasoning before RL optimization.

• Improved readability & coherence, which were weaknesses in DeepSeek-R1-Zero.

• Helped the model generalize across reasoning tasks before reinforcement learning fine-tuned performance.

Goal

• Establish clear, structured Chain-of-Thought (CoT) reasoning.

• Ensure logical coherence and readability before reinforcement learning.

• Prepare the model for reward-based optimization in later RL stages.

Step 2: Reinforcement Learning for Reasoning (GRPO)

After fine-tuning, the model underwent Reinforcement Learning (RL) using ~144K reasoning-specific samples, covering:

• Mathematical reasoning (AIME, MATH-500, CNMO)

• Logic-based deduction (formal reasoning, multi-step inference)

• Programming challenges (LeetCode, Codeforces, LiveCodeBench)

• Science problems (Physics, Chemistry, Engineering)

What Type of RL Was Used?

DeepSeek-R1 utilized Group Relative Policy Optimization (GRPO), a reinforcement learning method that improves reasoning efficiency without requiring a separate critic model.

• Why GRPO? Traditional RLHF (Reinforcement Learning from Human Feedback) relies on a reward model or critic, which can sometimes lead to reward hacking. Instead, GRPO directly optimizes the model by comparing sampled outputs, making it more stable and effective for reasoning tasks.

Reward System for RL Training

To refine the model’s reasoning, multiple reward signals were used:

• Accuracy Rewards: Prioritized correct answers, especially for math and programming problems.

• Format & Readability Rewards: Encouraged structured Chain-of-Thought (CoT) reasoning.

• Self-Reflection Rewards: Reinforced the ability to re-evaluate and correct its own mistakes.

• Language Consistency Rewards: Ensured responses remained in a single coherent language, avoiding mixed-language outputs.

Goal

• Improve multi-step reasoning and logical deduction.

• Develop self-reflection mechanisms to verify and refine answers.

• Enhance structured reasoning output while maintaining coherence.

Step 3: Supervised Fine-Tuning (SFT)

After Reinforcement Learning (RL) for Reasoning, the model underwent Supervised Fine-Tuning (SFT) to further enhance readability, coherence, factual alignment, and general usability.This phase ensured that DeepSeek-R1's responses were well-structured, user-friendly, and aligned with human expectations, reducing issues like verbosity or inconsistent reasoning.

After RL, the model was fine-tuned again using a large dataset (~800K samples) to improve readability, coherence, and factual knowledge.

Dataset Breakdown:

• 600K High-Quality Reasoning Samples

  • Derived from post-RL outputs — it was carefully curated through rejection sampling to ensure only high-quality, structured responses were retained.

  • Covered math, logic, science, and coding tasks.

• 200K General-Purpose Data

  • Included tasks like writing, factual QA, self-cognition, and translation.

Goal

• Enhance clarity, coherence, and logical structure in responses.

• Improve factual reliability and reduce inconsistencies.

• Expand beyond reasoning tasks to ensure a well-rounded AI model.

Step 4: Final Reinforcement Learning (RL) for General-Purpose Alignment

After Supervised Fine-Tuning (SFT), DeepSeek-R1 underwent a final RL stage focused on improving its alignment, factual accuracy, and overall helpfulness. Unlike the previous RL stage, which was specialized for reasoning, this phase refined the model’s behavior across all types of responses, ensuring it was reliable, safe, and well-aligned with human expectations.

What Was Optimized in This RL Stage?

To enhance general usability, DeepSeek-R1 was trained with reward signals focused on:

• Helpful & Structured Responses

  • Reinforced clarity, conciseness, and logical structure.

  • Ensured the model followed step-by-step reasoning when necessary and gave direct answers when appropriate.

• Harmlessness & Safety

  • Minimized biased, harmful, or unsafe outputs.

  • Avoided hallucinating information or generating misleading claims.

• Task-Specific Accuracy

  • Reinforced correct math, logic, and coding answers by applying task-based scoring.

  • Prevented "overconfidence" in uncertain answers, promoting self-verification techniques.

Goal

• Ensure human-like helpfulness, fairness, and factual correctness.

• Improve structured, safe, and aligned responses across all domains.

• Optimize the model for real-world applications beyond just reasoning tasks.

Step 5: Distillation for Smaller Models

To enable smaller models (Qwen, Llama) to inherit DeepSeek-R1’s reasoning skills, the researchers used distillation instead of RL.

Why Distillation Instead of RL?

• RL on smaller models is computationally expensive

• Distilled models outperform directly RL-trained small models

• Qwen-7B & Qwen-14B beat even larger models (32B) in reasoning tasks!

Final Outcome: Small models like Qwen-7B, Llama-8B, and Qwen-32B achieved high reasoning performance without requiring expensive RL training.

Why DeepSeek-R1 Matters for Open-Source AI?

• Bridges the gap between open-source & proprietary models

• Enables smaller, more efficient reasoning models

• Improves long-form problem-solving with RL + Distillation

DeepSeek-R1 is a huge step forward for open-source AI reasoning—and it's just getting started!

Read the research paper for more info on DeepSeek-R1

#AI #MachineLearning #DeepLearning #LLMs #ReinforcementLearning #DeepSeekR1 #OpenSourceAI