后训练（Post-Training）发生在预训练之 后，模型部署前或部署初期，后训练针对特定的任务或数据集进行额外训练，以优化模型性 能，包括 Supervised Fine-tuning（SFT，监督微调）和 Reinforcement Learning from Human Feedback（RLHF，人类反馈的强化学习）等环节。推理（Inference）是指在经过训练后， 将已经训练好的...
parser.add_argument("--lr_scheduler_type", type=str, default="constant_with_warmup", help="Type of learning rate scheduler") parser.add_argument("--warmup_steps", type=int, default=100, help="Number of warmup steps for learning rate scheduler") # LoRA 特定参数 parser.add_argument("-...
This paper investigates the decoding of two codes widely used in modern communication viz, Turbo Codes and Polar Codes using Deep Learning (DL) methods. The aim of this study is to explore the feasibility of using DL architectures based on Deep Neural Networks (DNN) and Recurrent Neural ...
DeepSeek-R1的训练过程可以分为以下四个阶段,分别是冷启动阶段(Cold Start),推理导向的强化学习(Reasoning-Oriented Reinforcement Learning),拒绝采样和监督微调(Rejection Sampling & Supervised Fine-Tuning)以及全场景强化学习(Reinforcement Learning for All Scenarios)。 在冷启动阶段,为了避免直接从基础模型进行强化学...
强化学习(Reinforcement Learning,RL) 五、评估结果 基准测试 英文能力 中文能力 数学能力 代码能力 六、讨论 SFT数据规模 强化学习的对齐税 在线强化学习 参考 一、技术介绍 随着LLM参数量持续地增加,其在训练和推理过程中面临着巨大的计算资源和低推理效率的挑战。尽管也出现了Grouped-Query Attention (GQA) 和 Mult...
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1. 技术架构 DeepSeek的模型架构以Transformer为核心，并针对效率与性能进行了优化：基础结构：采用Decoder...
Update:the Deep Learning Summer School videos are now online. Alright, let’s get started. 1. The need for distributed representations During his first talk, Yoshua Bengio said “This is my most important slide”. You can see that slide below: ...
Turbo AE Turbo Autoencoder code for paper: Y. Jiang, H. Kim, H. Asnani, S. Kannan, S. Oh, P. Viswanath, "Turbo Autoencoder: Deep learning based channel code for point-to-point communication channels" Conference on Neural Information Processing Systems (NeurIPS), Vancouver, December 2019...
强化学习(Reinforcement Learning,RL) GRPO算法 为了节省RL训练的成本,DeepSeek团队采用了Group Relative Policy Optimization (GRPO)算法。GRPO的核心创新在于它去除了PPO中的价值函数(critic),而是通过从策略模型中采样一组输出,然后计算这些输出的平均奖励作为基线。这种方法显著减少了训练资源的消耗,因为它不需要训练和...