DeepSeek-V3-技术报告（英）深度求索-2024-12-27

4.5.3 Batch-Wise Load Balance VS.Sequence-Wise Load Balance275 Post-Training285.1 Supervised Fine-Tuning285.2 Reinforcement Learning295.2.1 Reward Modell295.2.2 Group Relative Policy Optimization305.3 Evaluations305.3.1 Evaluation Settings305.3.2 Standard Evaluation325.3.3 Open-Ended Evaluation335.3.4 DeepSeek-V3 as a Generative Reward Model3354Disc1ss0m..············345.4.1 Distillation from DeepSeek-R1345.4.2 Self-Rewarding345.4.3 Multi-Token Prediction Evaluation356 Conclusion,Limitations,and Future Directions35A Contributions and Acknowledgments野B Ablation Studies for Low-Precision Training47B.1 FP8 v.s.BF16 Training47B.2 Discussion About Block-Wise Quantization47C Expert Specialization Patterns of the 16B Aux-Loss-Based and Aux-Loss-Free Models 483