DeepSeek-GRM - A general reward model jointly developed by DeepSeek and Tsinghua University

What is DeepSeek-GRM?

DeepSeek-GRM is a generalist reward model jointly proposed by DeepSeek and researchers from Tsinghua University. Through pointwise generative reward modeling (GRM) and self-principled critical tuning (SPCT), it significantly improves the quality of reward models and their scalability during inference. GRM outputs reward scores by generating structured evaluation text (including evaluation principles and detailed analysis of the answers), rather than directly outputting a single scalar value. DeepSeek-GRM performs exceptionally well on multiple comprehensive reward model benchmarks, significantly outperforming existing methods and several publicly available models. Its scalability during inference is particularly outstanding, with performance continuously improving as the number of samples increases.

Main functions of DeepSeek-GRM

• Intelligent question answering and dialogueDeepSeek can quickly answer various questions, covering scientific knowledge, history and culture, common sense, and technical issues. It can engage in intelligent dialogue with users, understand their intentions and emotions, and provide corresponding answers.
• Content generationIt can generate various types of content, including news reports, academic papers, business copy, and fictional stories.
• Data Analysis and VisualizationIt can process data such as Excel spreadsheets and CSV files, perform data cleaning and statistical analysis, and generate visual charts.
• Reasoning and logical abilityThey excel in mathematics and logical reasoning, are capable of multi-step reasoning and thinking, and can solve complex reasoning tasks.
• API IntegrationIt provides API interfaces, making it easy for developers to integrate it into their own applications and achieve a wider range of application scenarios.

The technical principles of DeepSeek-GRM

• Point-based Generative Reward Modeling (GRM)Instead of directly outputting a single scalar value, reward scores are output by generating structured evaluation text (including evaluation principles and detailed analysis of the responses). This increases the flexibility of the input and provides potential for expansion during inference.
• Self-Principle Evaluation and Optimization (SPCT)The GRM model is trained to adaptively generate high-quality evaluation principles and accurate comments through two stages: rejection-based fine-tuning and rule-based online reinforcement learning.
• Meta-RM (Meta-Reward Model)Used to evaluate the quality of the evaluation principles and comments generated by GRM, select high-quality samples for voting, and further improve the scalability of inference.
• Multi-Token Prediction (MTP)It supports the model in predicting multiple lexical units in a single forward propagation, improving training efficiency and inference speed.
• Relative strategy optimization(Group Relative Policy Optimization): A strategy to optimize the model by comparing the relative merits of different inference paths on the same task.
• Hybrid Expert Architecture (MoE)By dynamically selecting expert networks, unnecessary computation is reduced, improving the speed and flexibility of the model in handling complex tasks.
• FP8 Mixed Precision TrainingUsing more appropriate data precision during training reduces computation and saves time and costs.

DeepSeek-GRM project address

• arXiv technical paper:https://arxiv.org/pdf/2504.02495

Application scenarios of DeepSeek-GRM

• Precision agricultural managementBy using sensors to monitor parameters such as soil moisture and light intensity in real time, the system automatically adjusts irrigation and fertilization plans to improve resource utilization efficiency.
• Intelligent drivingIt uses deep learning models to process multi-source sensor data, enabling accurate environmental perception and decision-making.
• Natural Language Processing (NLP)This includes text generation, dialogue systems, machine translation, sentiment analysis, text classification, and information extraction.
• Code generation and understandingIt supports code auto-completion, code generation, code optimization, error detection and fixing, and supports multiple programming languages.
• Knowledge-based question answering and enhanced searchIt integrates with search engines to provide real-time and accurate knowledge Q&A.