Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
Implement a reasoning LLM in PyTorch from scratch, step by step
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
LeetCode for PyTorch — 65 ML/AI interview problems from real interviews at Google, Meta, Anthropic. Jupyter notebooks, an auto-grader, and an MCP AI tutor.
Multimodal RL training framework for diffusion & omni models
Halo is an open-source framework built by White Circle for training large language and multimodal models
Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
Awesome List for Agentic RL
A scalable, agentic-first, and HuggingFace-native RL framework for research (9k lines).
OpenJudge: A Unified Framework for Holistic Evaluation and Quality Rewards
FineEnvs — RL Environments 101: building and scaling RL environments in the age of LLMs
A curated list of papers on reinforcement learning post-training for LLMs
Robust recipes to align language models with human and AI preferences
The open source research environment for AI researchers to seamlessly train, evaluate, and scale models from local hardware to GPU clusters.
An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
Textbook on reinforcement learning from human feedback
Xtreme1 is an all-in-one data labeling and annotation platform for multimodal data training and supports 3D LiDAR point cloud, image, and LLM.
“AI-Compass”将为社区指引在 AI 技术海洋中航行的方向,无论你是初学者还是进阶开发者,都能在这里找到通往 AI 各大方向的路径。旨在帮助开发者系统性地了解 AI 的核心概念、主流技术、前沿趋势,并通过实践掌握从理论到落地的全过程。
A curated collection of papers, technical reports, frameworks, and tools for on-policy distillation (OPD) of large language models
Trinity-RFT is a general-purpose, flexible and scalable framework designed for reinforcement fine-tuning (RFT) of large language models (LLM).
An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale
Bilingual (中文+EN) ML / LLM / diffusion / agent interview cheat sheets for AI 秋招 — generated by ARIS /interview-cheatsheet, rendered by /render-html into single-file HTML, reads anywhere — plus a CV→DBLP-fact-checked academic homepage generator and hand-authored long-form blogs 🌱
A curated collection of papers and resources on On-Policy Distillation for Large Language Models.
Auditable human-feedback annotation, review, provenance, and frozen training-data export.
大模型算法 / 应用 / Infra 岗位面试资料:10 份文档、345 道题、约 36 万字。含字节 11 篇面经实录、大模型八股上下篇、RAG / Agent / 推理部署 / AI Infra 四大专题,以及 12 家大厂面经横向对比。
RewardBench: the first evaluation tool for reward models.