DeepSeek R1 Explained: Training Pipeline, GRPO Algorithm, and Benchmark Performance

Forum topic · 小凯 · 2026-03-27

Summary

DeepSeek R1 is a reasoning-enhanced open-source large language model from DeepSeek that matches OpenAI o1's reasoning performance while being free. This tutorial explains three core breakthroughs: R1-Zero, which demonstrated that reasoning can emerge from pure reinforcement learning without supervised fine-tuning; a new RL-based post-training paradigm built on the GRPO algorithm, which removes the value model used in PPO to cut memory and compute costs while improving training efficiency; and distillation of large-model reasoning ability into smaller models. The training pipeline uses two stages: reasoning-focused SFT with a small set of chain-of-thought data followed by RL on math, coding, science, and logic tasks, then a second stage with 600K reasoning samples plus 200K general data SFT and RL for safety and general capabilities. Reported benchmarks include AIME 2024 94.2, Codeforces rating 251, and MATH-500 90.8, with notably strong Chinese language understanding.

DeepSeek R1 Explained

1. Overview

What is DeepSeek R1?

DeepSeek R1 is a reasoning-enhanced large language model released by DeepSeek. Through innovative post-training techniques, it achieves reasoning capability comparable to OpenAI o1 while being completely free and open source.

Core breakthroughs

| Breakthrough | Description | |------|------| | R1-Zero | First to show that reasoning ability can emerge from pure reinforcement learning, without supervised learning | | New training paradigm | RL-based post-training built on the GRPO algorithm, significantly improving reasoning | | Distillation innovation | Distills large-model capability into smaller models, bringing reasoning to more scenarios |

Key achievements

≈ o1 level: reasoning capability on par with OpenAI o1
Completely free: a strong reasoning model available to everyone
Open source: technical approach and methods publicly released

---

2. The GRPO Algorithm

GRPO vs PPO

PPO (traditional) pipeline: 1. Input problem → 2. Generate output → 3. Reward evaluation → 4. Value prediction → 5. Compute advantage → 6. Update policy

GRPO (used in R1) pipeline: 1. Input problem → 2. Generate multiple candidates in parallel → 3. Compare rewards → 4. Select the best → 5. Optimize directly

Performance comparison

| Metric | PPO | GRPO | |------|-----|------| | Algorithm complexity | 85% | 45% | | Training efficiency | 60% | 90% | | Final performance | 70% | 85% |

Key advantages of GRPO

Higher compute efficiency: removing the Value Model greatly reduces memory and compute overhead
Better for open-ended problems: no reliance on a value model's judgment, better suited to complex reasoning tasks
Competitive optimization: candidates compete against each other, continuously improving model performance

---

3. Training Pipeline

Two-stage training strategy

Stage 1: Reasoning capability training

| Step | Content | Data | |------|------|------| | 1 | SFT on a small set of high-quality data | Thousands of chain-of-thought samples | | 2 | RL on reasoning-intensive tasks | Coding, math, science, and logic task sets |

Stage 2: Overall capability optimization

| Step | Content | Data | |------|------|------| | 1 | SFT on multi-domain data | 600K reasoning samples + 200K general samples | | 2 | RL for safety & general capability | Comprehensive evaluation system |

---

4. Benchmark Performance

Benchmark comparison

| Benchmark | GPT-4 | OpenAI o1 | DeepSeek R1 | |----------|-------|-----------|-------------| | AIME 2024 | 83.3 | 93.0 | 94.2 | | Codeforces | 11 | 213 | 251 | | MATH-500 | 42.5 | 85.5 | 90.8 |

Capability comparison

| Capability | DeepSeek R1 | OpenAI o1 | GPT-4 | |------|-------------|-----------|-------| | Math reasoning | 95 | 90 | 70 | | Coding | 92 | 88 | 75 | | Chinese understanding | 96 | 82 | 78 |

---

5. Technical Impact

Demonstrated the feasibility of pure reinforcement learning for reasoning
Novel application of the GRPO algorithm
Established a low-cost post-training paradigm
Performance parity between open-source and closed-source models

---

Source: Easy AI tutorial series