30 open-source projects similar to wangqinsi1/vision-zero, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
SeRL: Self-Play Reinforcement Learning for Large Language Models with Limited Data
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
TPAMI 2026 Ego-R1: Agentic Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM 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…
Yi-Hsin Hung 1 , Yueqi Duan 1 , Equal Contribution. 1 Tsinghua University NeurIPS 2025 (Spotlight)
This repository contains projects that aims to equip large-scale pretrained language models with better programming and reasoning skills. These projects are presented by Microsoft Research Asia and Microsoft Azure AI.
//: # (![Hugging Face Collection(https://img.shields.io/badge/Models-fcd022?style=for-the-badge&logo=huggingface&logoColor=000)]())
Jiwan Chung   Junhyeok Kim   Siyeol Kim   Jaeyoung Lee   Minsoo Kim   Youngjae Yu
💫SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation
⚙️ Algorithm Flow • 📊 Results ✨ Getting Started • 🏋️ Training • 🔧 Usage • 📃 Evaluation 🎈 Citation • 🌻 Acknowledgement • 📧 Contact • 📈 Star History
Visual-RFT: Visual Reinforcement Fine-Tuning Ziyu Liu · Zeyi Sun · Yuhang Zang · Xiaoyi Dong · Yuhang Cao · Haodong Duan · Dahua Lin · Jiaqi Wang Accepted By ICCV 2025! 📖 Paper | 🤗 Datasets | 🤗 Daily Paper 🌈We introduce Visual Reinforcement Fine-tuning (Visual-RFT) , the first comprehensive…
Implementation of the training framework proposed in Self-Rewarding Language Model , from MetaAI
Visionary-R1: Mitigating Shortcuts in Visual Reasoning with Reinforcement Learning A new RL method for visual reasoning, which significantly outperforms vanilla GRPO, and bypasses the need for explicit chain-of-thought supervision during training.
1. STaR 2. Mesh Transformer JAX 1. Updates 3. Pretrained Models 1. GPT-J-6B 1. Links 2. Acknowledgments 3. License 4. Model Details 5. Zero-Shot Evaluations 4. Architecture and Usage 1. Fine-tuning 2. JAX Dependency 5. TODO
Code for paper "LEVER: Learning to Verify Language-to-Code Generation with Execution". LEVER is a simple method that improves the code generation ability of large language models trained on code (CodeLMs), by learning to verify and rerank CodeLM-generated programs with their execution results.…
Trans0 aims to initialize a multilingual LLM as a translation agent via monolingual data. This is a public version with all in-house implementation replaced by huggingface trl.
Scaling RL to Long Videos Paper Yukang Chen , Wei Huang , Baifeng Shi, Qinghao Hu, Hanrong Ye, Ligeng Zhu, Zhijian Liu, Pavlo Molchanov, Jan Kautz, Xiaojuan Qi, Sifei Liu,Hongxu Yin, Yao Lu, Song Han
VLM-R1 is a reasoning vision-language model and embodied AI framework designed to map visual inputs and language instructions into physical navigation waypoints and robotic actions. It functions as a multimodal policy optimizer and an open vocabulary detector capable of locating objects based on arbitrary natural language descriptions. The system distinguishes itself through the use of chain-of-thought reasoning and reinforcement learning to solve complex visual and spatial tasks. It utilizes a video semantic memory system, which employs a visual cache to maintain a history of live video for
x 2025/09/26:🔥🔥🔥 We release our VideoChat-R1.5 model at Huggingface, paper, and eval code. - x 2025/09/22: 🎉🎉🎉 Our VideoChat-R1.5 is accepted by NIPS2025. - x 2025/04/22:🔥🔥🔥 We release our VideoChat-R1-caption at Huggingface. - x 2025/04/14:🔥🔥🔥 We release our VideoChat-R1 and…