Assignments for CS146S: The Modern Software Dev (Stanford University Fall 2025)

Official Implementation of "Maximum Likelihood Reinforcement Learning (MaxRL)"

A Zsh theme

🙃 A delightful community-driven (with 2,400+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python…

EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework based on veRL

Jax implementation of LMC-LSVI and Adam LMCDQN .

[ICLR 2026]QeRL enables RL for 32B LLMs on a single H100 GPU.

This repo contains the source code for the paper "Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning"

A scalable asynchronous reinforcement learning implementation with in-flight weight updates.

A playbook for systematically maximizing the performance of deep learning models.

🌾 OAT: A research-friendly framework for LLM online alignment, including reinforcement learning, preference learning, etc.

🙌 OpenHands: AI-Driven Development

Kinetics: Rethinking Test-Time Scaling Laws

SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning

A little Python script to collect LaTeX sources for upload to the arXiv.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

Solve puzzles. Improve your pytorch.

Simple RL training for reasoning

Minimal reproduction of DeepSeek R1-Zero

verl: Volcano Engine Reinforcement Learning for LLMs

Code for the paper "VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment"

Recipes to train reward model for RLHF.

A high-throughput and memory-efficient inference and serving engine for LLMs

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

Python best practices guidebook, written for humans.