Open-source alternatives to X R1

From Chain-of-Thought prompting to OpenAI o1 and DeepSeek-R1 🍓

Baichuan-M1-14B

Exploring Applications of GRPO

DeepSeek 系列工作解读、扩展和复现。

🚀 Reinforcement Learning for Language Agents🌟

DeepSeek-R1 is an open-weights large language model focused on advanced reasoning. It uses chain-of-thought processing and internal monologues to solve complex mathematical and logical problems by breaking tasks into sequential, verifiable thought processes. The model is developed using reinforcement learning to optimize reasoning patterns and verify logical steps. It employs a distillation process to transfer these high-performance logic capabilities from a large teacher model into smaller, computationally efficient versions. The training framework incorporates group relative policy optimiz

Project Page For "Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement"

Checkpoints take up a lot of space. Please email yninghong@gmail.com if you need them.

🧐 About | 🚀 Quick Start | 🐣 Agentless Mini | 📝 Citation | 🙏 Acknowledgements

R1-onevision, a visual language model capable of deep CoT reasoning.

OpenSeek aims to unite the global open source community to drive collaborative innovation in algorithms, data and systems to develop next-generation models.

Medical o1, Towards medical complex reasoning with LLMs

📄 Paper | 🌐 Dataset (v2) | 📘 Model (v2)

O1 Replication Journey

A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 🍓 and reasoning techniques.

EasyR1 is a distributed model training system and reinforcement learning framework for large language and vision-language models. It functions as a multimodal trainer and an implementation of a Proximal Policy Optimization pipeline designed to refine the reasoning and perception capabilities of models that process both text and images. The system specializes in distributing reinforcement learning workloads across multiple compute nodes to manage high memory requirements. It optimizes hardware utilization through padding-free training and fine-tuning to fit large models onto available graphics