Search-R1 is a reinforcement learning framework designed to train large language models (LLMs) capable of reasoning and calling search engines. Built upon veRL, it supports various reinforcement learning methods and different LLM architectures, enabling efficiency and scalability in tool-augmented reasoning research and development.

Persona Engine is an AI-powered interactive avatar engine integrating Live2D, LLM, ASR, TTS, and RVC, designed to provide a smooth interactive experience for VTubing, live streaming, and virtual assistant applications. The engine uses AI technology to give virtual characters vivid personality and expressiveness, making it possible for applications in gaming, social media, and education. This product is suitable for developers and content creators who wish to create vivid, interactive characters, offering ease of use and integration, seamlessly connecting with existing streaming software.

InstantCharacter is a character personalization framework based on diffusion transformers, designed to overcome the limitations of existing learning-based customization methods. The framework's main advantages lie in its open-domain personalization, high-fidelity results, and effective character feature processing capabilities, suitable for generating various character appearances, poses, and styles. The framework utilizes a large-scale dataset containing tens of millions of samples for training to achieve both character consistency and text editability optimization. This technology sets a new benchmark for character-driven image generation.

Arthur Engine is a tool designed to monitor and govern AI/ML workloads, leveraging popular open-source technologies and frameworks. The enterprise version of this product offers enhanced performance and additional features such as customized enterprise-grade safeguards and metrics, aiming to maximize AI's potential for organizations. It effectively evaluates and optimizes models, ensuring data security and compliance.

Factorio Learning Environment (FLE) is a novel framework built on the game Factorio, used to evaluate the capabilities of large language models (LLMs) in long-term planning, program synthesis, and resource optimization. As LLMs gradually saturate existing benchmark tests, FLE provides a new open-ended evaluation approach. Its importance lies in enabling researchers to gain a more comprehensive and in-depth understanding of the strengths and weaknesses of LLMs. Key advantages include open-ended challenges with exponentially increasing difficulty, and two evaluation protocols: structured tasks and open-ended tasks. This project was developed by Jack Hopkins et al., released as open source, free to use, and aims to promote research by AI researchers on the capabilities of agents in complex, open-ended domains.

Light-R1 is an open-source project developed by Qihoo360, aiming to train long-chain reasoning models through curriculum-style supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL). This project achieves long-chain reasoning capabilities from scratch through decontaminated datasets and efficient training methods. Its main advantages include open-source training data, low-cost training, and excellent performance in mathematical reasoning. The project background is based on the current training needs of long-chain reasoning models, aiming to provide a transparent and reproducible training method. The project is currently free and open-source, suitable for research institutions and developers.

Awesome-LLM-Post-training is a repository focusing on large language model (LLM) post-training methods. It provides in-depth research on LLM post-training, including tutorials, surveys, and guides. This repository is based on the paper "LLM Post-Training: A Deep Dive into Reasoning Large Language Models" and aims to help researchers and developers better understand and apply LLM post-training techniques. This repository is freely available and suitable for both academic research and industrial applications.

Flux is a high-performance communication overlap library developed by ByteDance, designed for tensor and expert parallelism on GPUs. Through efficient kernels and compatibility with PyTorch, it supports various parallelization strategies and is suitable for large-scale model training and inference. Flux's main advantages include high performance, ease of integration, and support for multiple NVIDIA GPU architectures. It excels in large-scale distributed training, particularly with Mixture-of-Experts (MoE) models, significantly improving computational efficiency.

Atom of Thoughts (AoT) is a novel reasoning framework that transforms the reasoning process into a Markov process by representing solutions as a combination of atomic problems. This framework significantly improves the performance of large language models on reasoning tasks through decomposition and contraction mechanisms, while reducing wasted computing resources. AoT can be used as an independent reasoning method or as a plugin for existing test-time augmentation methods, flexibly combining the advantages of different methods. This framework is open-source and implemented in Python, making it suitable for researchers and developers to experiment with and apply in the fields of natural language processing and large language models.

Search-R1 is a reinforcement learning framework designed to train large language models (LLMs) capable of reasoning and calling search engines. Built upon veRL, it supports various reinforcement learning methods and different LLM architectures, enabling efficiency and scalability in tool-augmented reasoning research and development.

Persona Engine is an AI-powered interactive avatar engine integrating Live2D, LLM, ASR, TTS, and RVC, designed to provide a smooth interactive experience for VTubing, live streaming, and virtual assistant applications. The engine uses AI technology to give virtual characters vivid personality and expressiveness, making it possible for applications in gaming, social media, and education. This product is suitable for developers and content creators who wish to create vivid, interactive characters, offering ease of use and integration, seamlessly connecting with existing streaming software.

InstantCharacter is a character personalization framework based on diffusion transformers, designed to overcome the limitations of existing learning-based customization methods. The framework's main advantages lie in its open-domain personalization, high-fidelity results, and effective character feature processing capabilities, suitable for generating various character appearances, poses, and styles. The framework utilizes a large-scale dataset containing tens of millions of samples for training to achieve both character consistency and text editability optimization. This technology sets a new benchmark for character-driven image generation.

GenPRM is an emerging process reward model (PRM) that improves computational efficiency during testing through generative reasoning. This technology provides more accurate reward assessments when handling complex tasks and is suitable for applications in various machine learning and artificial intelligence fields. Its main advantages are the ability to optimize model performance with limited resources and reduce computational costs in practical applications.

Arthur Engine is a tool designed to monitor and govern AI/ML workloads, leveraging popular open-source technologies and frameworks. The enterprise version of this product offers enhanced performance and additional features such as customized enterprise-grade safeguards and metrics, aiming to maximize AI's potential for organizations. It effectively evaluates and optimizes models, ensuring data security and compliance.

MoCha is an innovative technology designed to synthesize high-quality dialogue characters for widespread application in film production, games, and animation. The main advantage of this technology is its ability to generate more natural and fluent character dialogue, enhancing audience immersion. MoCha is positioned in the market for professional film production companies and independent developers, aiming to improve the realism of character interaction. The product uses a deep learning-based model, with a paid pricing strategy offering different tiers of service packages.

Factorio Learning Environment (FLE) is a novel framework built on the game Factorio, used to evaluate the capabilities of large language models (LLMs) in long-term planning, program synthesis, and resource optimization. As LLMs gradually saturate existing benchmark tests, FLE provides a new open-ended evaluation approach. Its importance lies in enabling researchers to gain a more comprehensive and in-depth understanding of the strengths and weaknesses of LLMs. Key advantages include open-ended challenges with exponentially increasing difficulty, and two evaluation protocols: structured tasks and open-ended tasks. This project was developed by Jack Hopkins et al., released as open source, free to use, and aims to promote research by AI researchers on the capabilities of agents in complex, open-ended domains.

Light-R1 is an open-source project developed by Qihoo360, aiming to train long-chain reasoning models through curriculum-style supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (RL). This project achieves long-chain reasoning capabilities from scratch through decontaminated datasets and efficient training methods. Its main advantages include open-source training data, low-cost training, and excellent performance in mathematical reasoning. The project background is based on the current training needs of long-chain reasoning models, aiming to provide a transparent and reproducible training method. The project is currently free and open-source, suitable for research institutions and developers.

Awesome-LLM-Post-training is a repository focusing on large language model (LLM) post-training methods. It provides in-depth research on LLM post-training, including tutorials, surveys, and guides. This repository is based on the paper "LLM Post-Training: A Deep Dive into Reasoning Large Language Models" and aims to help researchers and developers better understand and apply LLM post-training techniques. This repository is freely available and suitable for both academic research and industrial applications.