Peking University's "DeepSeek-R1 and Strong Inference Model Development Explained" (PDF file) - AI Tutorial Materials

This article provides an in-depth analysis of DeepSeek-R1 and the development of strong inference models. It details the technical architecture of DeepSeek-R1, including its rule-based reward mechanism, Group Relative Policy Optimization (GRPO) algorithm, and multi-stage training process, revealing its optimization strategies in terms of inference ability, language consistency, and security. The article also discusses the social and economic benefits of DeepSeek-R1 and analyzes its impact on...MultimodalThe application potential in various scenarios is explored, and future technological development directions such as modal penetration, formal verification, and audit alignment are discussed. A comprehensive and systematic perspective is provided for a deeper understanding of DeepSeek-R1's technological innovations and the development of its strong inference models.

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DeepSeek-R1 and Strong Inference Model Development Explained

• This paper introduces the main research directions of large language model alignment and scalable supervision, focusing on the development background and significance of DeepSeek-R1, Kimi 1.5 and similar strong inference models.

DeepSeek-R1 pioneers a new paradigm of strong reasoning and slow thinking powered by RL.

• This paper delves into how DeepSeek-R1, supported by reinforcement learning (RL), pioneers a new paradigm of strong reasoning and slow thinking. It discusses its outstanding performance in mathematical coding tasks, knowledge-based question answering, and long text-dependent tasks, and compares it with Open...AI o1 series models.

DeepSeek-R1 Technology Analysis

• DeepSeek-R1 Zero

  This article provides a detailed analysis of the technical aspects of DeepSeek-R1 Zero as a pure reinforcement learning-driven strong inference model that employs unsupervised fine-tuning (SFT), including reward modeling, training templates, and key insights.

• DeepSeek-R1 Technology Pipeline Overview

  The presentation showcases the overall workflow of DeepSeek-R1 technology, covering the multi-stage training process from DeepSeek-V3 Base to the final model, including cold start, inference-centric reinforcement learning, rejection sampling, and full-domain SFT.

Insights & Takeaways Behind DeepSeek-R1

• This paper summarizes key insights and technical highlights from the development of DeepSeek-R1, such as pure RL development of inference capabilities, the advantages of multi-stage training, inference-centric RL training, and GRPO-enabled RL-Scale.

Social and economic benefits of DeepSeek-R1

• This study explores the potential impact of DeepSeek-R1 on social and economic fields, including the exploration of low-cost, high-quality language models, its application prospects in vertical and horizontal sectors, its impact on capital markets, resource optimization, and market activation.High efficiencyInnovation and other aspects.

Technical Comparison and Discussion

• STaR-based Methods vs. RL-based Methods

  This study compares the advantages and disadvantages of STaR-based (Bootstrapping Reasoning With Reasoning) and reinforcement learning-based methods on strong reasoning paths.

• Distillation vs. Reinforcement Learning Driven

  This study analyzes the different strategies and effects of model distillation and reinforcement learning in improving the strong reasoning ability of models, and explores their respective advantages and limitations.

• The role of PRM & MCTS

  This paper discusses the application of PRM (Preference Reward Model) and MCTS (Monte Carlo Tree Search) in strong inference models and the challenges they face.

• From text modality toMultimodal

  Exploring strong reasoning models from text modality toMultimodalThe possibilities for expansion and the challenges faced, and a look at the potential of modal penetration and modal linkage to enhance strong reasoning capabilities.

• Other discussions: Over-Thinking, etc.

  This study analyzes the over-thinking phenomenon that may occur in strong inference models and its impact on the training and inference processes, and explores how to reasonably allocate Test-Time Compute to optimize model performance.

Analysis and Discussion of Future Directions

• Modal penetration enables inference boundary expansion: Align-DS-V

  This paper explores how modal penetration technology can enable the expansion of inference boundaries and looks forward to the application prospects of technologies such as Align-DS-V in future strong inference models.

• Synthetic data and Test-Time Scaling

  This study examines the potential and importance of synthetic data and Test-Time Scaling in overcoming the pitfalls of data reproduction and improving model performance.

• Security under strong inference: Aligning formal verification with auditing

  This paper discusses how to ensure the security and reliability of strong inference models through techniques such as formal verification and audit alignment.

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