tracked author

Haibin Lin

ByteDance Seed LLM systems researcher; homepage and GitHub both connect Haibin Lin to VERL and the eric_haibin_lin X handle. Recurs across HybridFlow, DAPO, MegaScale, MegaScale-MoE, and Laminar system lines.

4 archived notes X: high Homepage X

Related Notes

按论文归档时间排序，展示该作者在本站已经出现的材料。

归档 Jun 28, 2026

Laminar: A Scalable Asynchronous RL Post Training Framework

让完成轨迹独立进入 experience buffer，并以 CPU/RDMA relay 和同版本 repack 解除全局 batch / 权重同步 barrier。

待审阅 2510.12633-laminar-asynchronous-rl-post-training RL Infrastructure Rollout Optimization Distributed Training

归档 Jun 28, 2026

MegaScale MoE: Large Scale Communication Efficient Training of Mixture of Experts Models in Production

围绕 attention SP、FFN EP、communication overlap 和 compressed communication 重构生产 MoE 训练路径。

待审阅 2505.11432-megascale-moe-communication-efficient-training MoE Systems Distributed Training Training Memory

归档 Mar 03, 2026

DAPO: An Open Source LLM Reinforcement Learning System at Scale

用 Clip Higher、Dynamic Sampling、token level loss 与 overlong shaping 构成可复现 long CoT RL recipe。

待审阅 2503.14476-dapo-long-cot-rl-system RL Algorithm Reasoning RL RL Infrastructure

归档 Jan 27, 2026

HybridFlow: A Flexible and Efficient RLHF Framework

用跨模型 single controller 与模型内 multi controller 统一编排 RLHF dataflow 和并行执行。

待审阅 2409.19256-hybridflow-rlhf-framework RL Infrastructure Distributed Training Rollout Optimization