Deepseek背景综述及在金融领域应用场景初探

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]