Core Insights - The article discusses the rapid advancements in AI, particularly focusing on DeepSeek's open-source strategy and the release of OpenAI's GPT-4.5 model, highlighting the competitive landscape in the AI large model sector [1][9]. Group 1: DeepSeek's Open-Source Strategy - DeepSeek, established in 2023, has released several products, including DeepSeek R1, which offers performance comparable to leading closed-source models at a significantly lower training cost of approximately $557.6 million [2][5]. - The open-source initiative by DeepSeek, including the release of code libraries like FlashMLA and DeepEP, aims to lower the development barrier for AI models and enhance computational efficiency [5][6]. - The performance of DeepSeek R1 has led to a rapid user growth of 100 million within just seven days post-launch, marking it as the fastest-growing AI application globally [7]. Group 2: Global AI Large Model Progress - The AI large model sector is experiencing significant growth, with DeepSeek's low-cost models challenging existing players like Kimi, which saw only a 28% increase in active users compared to DeepSeek's 750% growth [7]. - OpenAI's GPT-4.5, released on February 28, 2025, is touted as the largest and most knowledgeable chat model to date, with a high cost structure that raises questions about its performance relative to its price [9][10]. - The competitive landscape is shifting, with DeepSeek's open-source approach prompting other companies, including OpenAI, to consider similar strategies to maintain market relevance [13]. Group 3: AI Large Model Investment Dynamics - The emergence of low-cost, high-performance models like those from DeepSeek is reshaping investment dynamics, allowing smaller firms to enter the market and focus on innovation rather than heavy capital investment [14][15]. - The article notes a trend where investment focus is shifting from infrastructure to application scenarios, with significant funding opportunities in vertical applications such as finance and healthcare [15]. - Recent funding events in the AI large model sector indicate a growing interest, with several companies securing substantial investments, reflecting the market's evolving landscape [16][17].

Quantitative Models and Construction Methods Model Name: DeepSeek-R1 - **Model Construction Idea**: The DeepSeek-R1 model leverages a mixture of experts (MoE) architecture and dynamic routing technology to reduce inference costs while maintaining high performance[16] - **Model Construction Process**: - **Mixture of Experts (MoE)**: Integrates multiple "expert" models to enhance overall model performance. A gating network determines which expert(s) should handle specific inputs[27] - **Group Relative Policy Optimization (GRPO)**: Eliminates the need for a separate critic model in reinforcement learning, reducing training costs by using group scores to estimate the baseline[31] - **Self-evolution Process**: The model improves its reasoning capabilities through reinforcement learning, exhibiting complex behaviors like reflection and exploration of alternative approaches[39][41] - **Cold Start**: Introduces high-quality long CoT data to stabilize the model during the initial training phase[42] - **Model Evaluation**: The model demonstrates significant cost efficiency and high performance, making it a groundbreaking development in AI applications[16][43] Model Name: DeepSeek-V2 - **Model Construction Idea**: The DeepSeek-V2 model is a powerful MoE language model designed with innovative architectures like Multi-head Latent Attention (MLA)[23] - **Model Construction Process**: - **Multi-head Latent Attention (MLA)**: Improves performance over traditional Multi-head Attention (MHA) by reducing KV cache, enhancing inference efficiency[25] - **Mixture of Experts (MoE)**: Similar to DeepSeek-R1, it uses a gating network to activate specific experts based on input, optimizing resource usage and performance[27] - **Model Evaluation**: The model shows advantages in performance, training cost, and inference efficiency, making it a strong, economical, and efficient language model[23][27] Model Name: DeepSeek-V3 - **Model Construction Idea**: The DeepSeek-V3 model aims to enhance open-source model performance and push towards general artificial intelligence[33] - **Model Construction Process**: - **Multi-Token Prediction (MTP)**: Enhances model performance by predicting multiple future tokens at each position, increasing training signal density[34] - **FP8 Mixed Precision Training**: Improves computational efficiency and reduces memory usage while maintaining model accuracy by using lower precision data types[36] - **Model Evaluation**: The model effectively balances computational efficiency and performance, making it suitable for large-scale model training[33][36] Model Backtesting Results - **DeepSeek-R1**: Demonstrates significant cost efficiency, achieving performance comparable to ChatGPT-01 with much lower training costs[43] - **DeepSeek-V2**: Shows superior performance and efficiency in training and inference compared to traditional models[23][27] - **DeepSeek-V3**: Achieves high computational efficiency and maintains model accuracy, making it effective for large-scale training[33][36] Quantitative Factors and Construction Methods Factor Name: Scaling Laws - **Factor Construction Idea**: Describes the predictable relationship between model performance and the scale of model parameters, training data, and computational resources[21] - **Factor Construction Process**: - **Scaling Laws**: As model parameters, training data, and computational resources increase, model performance improves in a predictable manner[21] - **Data Quality**: High-quality data shifts the optimal allocation strategy towards model expansion[22] - **Factor Evaluation**: Provides a strong guideline for resource planning and model performance optimization[21][22] Factor Backtesting Results - **Scaling Laws**: Demonstrates a predictable improvement in model performance with increased resources, validating the factor's effectiveness in guiding model development[21][22]

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