ByteDance Research has released DAPO (Dynamic Sampling Policy Optimization), an open-source reinforcement learning system for LLMs, aiming to improve reasoning abilities and address reproducibility issues. DAPO includes innovations like Clip-Higher, Dynamic Sampling, Token-level Policy Gradient Loss, and Overlong Reward Shaping, achieving a score of 50 on the AIME 2024 benchmark with the Qwen2.5-32B model.
Goose is a local, extensible, open-source AI agent designed to automate complex engineering tasks. It can build projects from scratch, write and execute code, debug failures, orchestrate workflows, and interact with external APIs. Goose is flexible, supporting any LLM and seamlessly integrating with MCP-enabled APIs, making it a powerful tool for developers to accelerate innovation.
AGNTCY is building the Internet of Agents to be accessible for all, focusing on innovation, development, and maintenance of software components and services for agentic workflows and multi-agent applications.
Discover:
1. Agent directory
- Registry for agent publishing and discovery
- Tracks reputation and quality
2. Open agent schema framework
- Standard metadata format for agent capabilities
- Verification for agent providers
- Specification at github.com/agntcy/oasf
Compose:
1. Agent connect protocol and SDK
- Standardized agent communication across frameworks
- Manages message passing, state, and context
- Specification at github.com/agntcy/acp-spec
What could these look like in action? A developer can find suitable agents in the directory (using OASF) and enable their communication with the agent connect protocol, regardless of frameworks.
AGNTCY is an open-source collective building infrastructure for AI agents to collaborate, led by Cisco, LangChain, Galileo, and other contributors. The initiative aims to create an open, interoperable foundation for agentic AI systems to work together seamlessly across different frameworks and vendors.
AGNTCY plans to develop key components such as an agent directory, an open agent schema framework, and an agent connect protocol to facilitate this interoperability.
Hugging Face's initiative to replicate DeepSeek-R1, focusing on developing datasets and sharing training pipelines for reasoning models.
The article introduces Hugging Face's Open-R1 project, a community-driven initiative to reconstruct and expand upon DeepSeek-R1, a cutting-edge reasoning language model. DeepSeek-R1, which emerged as a significant breakthrough, utilizes pure reinforcement learning to enhance a base model's reasoning capabilities without human supervision. However, DeepSeek did not release the datasets, training code, or detailed hyperparameters used to create the model, leaving key aspects of its development opaque.
The Open-R1 project aims to address these gaps by systematically replicating and improving upon DeepSeek-R1's methodology. The initiative involves three main steps:
- Replicating the Reasoning Dataset: Creating a reasoning dataset by distilling knowledge from DeepSeek-R1.
- Reconstructing the Reinforcement Learning Pipeline: Developing a pure RL pipeline, including large-scale datasets for math, reasoning, and coding.
- Demonstrating Multi-Stage Training: Showing how to transition from a base model to supervised fine-tuning (SFT) and then to RL, providing a comprehensive training framework.
The article provides a comprehensive introduction to large language models (LLMs), explaining their purpose, how they function, and their applications. It covers various types of LLMs, including general-purpose and task-specific models, and discusses the distinction between closed-source and open-source LLMs. The article also explores the ethical considerations of building and using LLMs and the future possibilities for these models.
Tabby is an open-source, self-hosted AI coding assistant that is easy to configure and deploy with a simple TOML config. It is powered by Rust for speed and safety.
An article discussing the use of embeddings in natural language processing, focusing on comparing open source and closed source embedding models for semantic search, including techniques like clustering and re-ranking.
