# liger

Published articles for liger.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation

DevFeed: [How LinkedIn Trains AI Job Search 8x Faster with Multi-Teacher Distillation](<https://devfeed.tech/articles/how-linkedin-trains-ai-job-search-8x-faster-with-multi-teacher-distillation-8453.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/09/linkedin-ai-multi-teacher/>)

Author: Claudio Masolo

Published: 2026-09-11T10:00:00Z

Content type: news

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [sglang](<https://devfeed.tech/topics/sglang.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [agentic-ai-architecture](<https://devfeed.tech/tags/agentic-ai-architecture.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [latency](<https://devfeed.tech/tags/latency.md>), [liger](<https://devfeed.tech/tags/liger.md>), [linkedin](<https://devfeed.tech/tags/linkedin.md>), [linkedin-ai-multi-teacher](<https://devfeed.tech/tags/linkedin-ai-multi-teacher.md>), [llms](<https://devfeed.tech/tags/llms.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [news](<https://devfeed.tech/tags/news.md>), [search](<https://devfeed.tech/tags/search.md>), [sglang](<https://devfeed.tech/tags/sglang.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

LinkedIn describes a multi-teacher distillation pipeline for AI-powered job search that trains a 0.6B-parameter ranking model. The article focuses on SGLang-based teacher serving, online and offline distillation, and training optimizations reported to produce roughly an eightfold speedup.

### Source excerpt

LinkedIn has published details of the training infrastructure behind its AI-powered job search, describing a multi-teacher distillation pipeline that compresses knowledge from large teacher models into a compact 0.6B-parameter ranking model. By Claudio Masolo

## Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP

DevFeed: [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP](<https://devfeed.tech/articles/profiling-in-pytorch-part-2-from-nn-linear-to-a-fused-mlp-7522.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/torch-mlp-fusion>)

Author: Aritra Roy Gosthipaty; Rémi Ouazan Reboul; Sergio Paniego; Pedro Cuenca; Sayak Paul

Published: 2026-06-11T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [jobs](<https://devfeed.tech/topics/jobs.md>)

Tags: [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [kernels](<https://devfeed.tech/tags/kernels.md>), [liger](<https://devfeed.tech/tags/liger.md>), [mlp](<https://devfeed.tech/tags/mlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [profile](<https://devfeed.tech/tags/profile.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [torch](<https://devfeed.tech/tags/torch.md>), [traces](<https://devfeed.tech/tags/traces.md>)

### AI overview

A PyTorch profiling tutorial that moves from nn.Linear to a fused multilayer perceptron, explaining GPU kernels, CPU launch overhead, and tensor-transpose behavior in profiler traces.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## 🐯 Liger GRPO meets TRL

DevFeed: [🐯 Liger GRPO meets TRL](<https://devfeed.tech/articles/liger-grpo-meets-trl-7332.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/liger-grpo>)

Author: Shivam Sahni; Kashif Rasul; Salman Mohammadi; Shirin Yamani; Yanning Chen; Liberty

Published: 2025-05-25T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [grpo](<https://devfeed.tech/topics/grpo.md>), [trl](<https://devfeed.tech/topics/trl.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [rlhf](<https://devfeed.tech/topics/rlhf.md>), [coding](<https://devfeed.tech/topics/coding.md>), [math](<https://devfeed.tech/topics/math.md>)

Tags: [coding](<https://devfeed.tech/tags/coding.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [liger](<https://devfeed.tech/tags/liger.md>), [llm](<https://devfeed.tech/tags/llm.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [performance](<https://devfeed.tech/tags/performance.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rlhf](<https://devfeed.tech/tags/rlhf.md>), [trl](<https://devfeed.tech/tags/trl.md>)

### AI overview

This article explains how Group Relative Policy Optimization (GRPO) can reduce the resource requirements of reinforcement learning fine-tuning for language models. It presents a TRL optimization based on chunked GRPO loss that reduces peak memory usage by 40% and discusses scaling GRPO across multiple GPUs and nodes while preserving performance and correctness.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.