Projects sharing features with Video R1

Qwen2.5 is a suite of large language model foundation models designed for natural language generation, code production, and complex mathematical reasoning. The project encompasses a multilingual language model capable of processing dozens of languages and a specialized code generation model for technical problem solving and debugging. The framework is distinguished by its long context capabilities, enabling the analysis of massive inputs ranging from 256K up to 1 million tokens. It further functions as an agentic framework, utilizing standardized templates and parsers to execute autonomous wo

Janus is a multimodal large language model and unified framework that integrates visual understanding and image generation within a single neural network. It functions as both a visual understanding model for analyzing images and a text-to-image generator. The system uses a unified transformer backbone and a multimodal latent space to bridge the gap between text and visual data. This architecture employs decoupled visual encoding and cross-modal tokenization to separate the paths for discriminative understanding and generative tasks, representing images as grids of discrete codes. The projec

Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency

Exploring Applications of GRPO

DeepSeek 系列工作解读、扩展和复现。

Baichuan-M1-14B

An Open Large Reasoning Model for Real-World Solutions

Yi-Hsin Hung 1 , Yueqi Duan 1 , Equal Contribution. 1 Tsinghua University NeurIPS 2025 (Spotlight)

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

TPAMI 2026 Ego-R1: Agentic Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning

Grounded Reasoning wiht Texts and Images (GRIT) is a novel method for training Multimodal Large Language Models (MLLMs) to perform grounded reasoning by generating reasoning chains that interleave natural language and explicit bounding box coordinates. This approach can use as few as 20 training…

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.

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