tracked author
Damai Dai
Math-Shepherd coauthor and DeepSeek MoE / LLM researcher. OpenReview records DeepSeek-AI researcher, Peking University PhD and undergraduate background, and Zhifang Sui as PhD advisor. No high-confidence personal X account found in this pass.
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DSpark: Confidence Scheduled Speculative Decoding with Semi Autoregressive Generation
DSpark 是 DeepSeek 把并行 drafter 推向生产 serving 的一套完整方案：用 DFlash 式 parallel backbone 先一次生成长候选块，再用低秩 Markov head 注入块内局部自回归依赖，随后用 calibrated confidence head 和硬件感知 prefix scheduler 按请求与负载动态裁剪 target verification 长度。离线 Qwen3 / G...
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
DeepSeekMoE 把 MoE (Mixture of Experts，混合专家) 的效率问题改写成专家专门化问题：fine grained expert segmentation 通过切小 FFN (Feed Forward Network，前馈网络) 专家并增加激活专家数，提高每个 token 的专家组合分辨率；shared expert isolation 通过固定激活共享专家承载通用知识，让 routed experts ...
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models
Engram 把大模型稀疏性从 MoE (Mixture of Experts，混合专家，用条件计算扩大 total 参数并控制 active compute) 扩展到 conditional memory：用 hashed N gram lookup 存静态局部模式，用 context aware gate 决定是否把查到的记忆注入 hidden state；在 iso parameter / iso FLOPs 的 27B MoE...
DeepSeek V3 Technical Report
DeepSeek V3 用 MLA (Multi head Latent Attention，多头潜变量注意力)、DeepSeekMoE、无辅助损失负载均衡、MTP (Multi Token Prediction，多 token 预测)、FP8 混合精度训练和 DualPipe 通信重叠，把 671B total / 37B active 的开放 MoE (Mixture of Experts，混合专家) 模型训练到强代码、数学和通用...
Math Shepherd: Verify and Reinforce LLMs Step by step without Human Annotations
Math Shepherd 的核心价值在于把数学推理步骤的标注问题改写为“当前 step 之后还能否补全到正确答案”的 Monte Carlo potential estimation：对每个中间 step 采样多个 continuation，用最终答案正确性给 step 生成 hard / soft pseudo label，训练 PRM 做 verifier reranking，并进一步把 PRM reward 接入 step b...