阿里巴巴阿里控股-AI 算法工程师-杭州
社招全职2年以上地点：杭州状态：招聘♡ 收藏
工作描述
Qualifications 1.扎实的 LLM 知识：Transformer、预训练/SFT/RLHF、DPO、GRPO/PPO、Reward Model，熟练 PyTorch，熟悉 HuggingFace、vLLM / SGLang 等推理框架。 2.Prompt 与 Agent 应用开发经验：多步推理、Tool-…
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包括英文材料
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
SFT+
https://cameronrwolfe.substack.com/p/understanding-and-using-supervised
Understanding how SFT works from the idea to a working implementation...
RLHF+
[英文] What is RLHF?
https://aws.amazon.com/what-is/reinforcement-learning-from-human-feedback/
Reinforcement learning from human feedback (RLHF) is a machine learning (ML) technique that uses human feedback to optimize ML models to self-learn more efficiently.
https://www.ibm.com/think/topics/rlhf
Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a “reward model” is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning.
GRPO+
https://cameronrwolfe.substack.com/p/grpo
Most early work on RL for LLMs used Proximal Policy Optimization (PPO) as the default RL optimizer, but recent reasoning research relies upon Group Relative Policy Optimization (GRPO).
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