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🧠A flexible, efficient, and production-ready post-training reinforcement learning framework for LLMs
A flexible, efficient, and production-ready post-training reinforcement learning framework for LLMs
Flexible and scalable reinforcement learning training infrastructure for embodied and agentic AI post-training, decoupling logical workflow composition from efficient physical execution via the M2Flow paradigm.
An open-source orchestration and optimization toolkit for AI Agents that connects, observes, and tunes multi-agent systems in a framework-agnostic manner, with MCP/A2A protocol support, end-to-end profiling, offline evaluation, and RL fine-tuning.
An RL training environment building library for LLMs, providing complete infrastructure from development and testing to scaled rollout collection, with built-in RLVR scenarios and tool-calling support.
An open-source framework for building, evaluating, and training general multi-agent systems. Features natural language agent creation, distributed reinforcement learning training pipeline, and complex environment interactions. Ranks top on authoritative benchmarks including GAIA, OSWorld, and VisualWebArena.
A generative agent framework inspired by human dual-process theory, combining fast and slow thinking mechanisms with in-context reinforcement learning to efficiently solve complex interactive reasoning tasks.
An LLM post-training framework for RL scaling by Tsinghua THUDM, deeply integrating Megatron-LM training with SGLang inference engine for distributed reinforcement learning on large models like GLM, Qwen, DeepSeek, and Llama.
AI-Compass is a comprehensive open-source project that provides learning paths and practical guidelines for AI technologies, helping users from beginners to professionals build a complete AI knowledge system from fundamental theory to cutting-edge applications.
A curated collection of autonomous agents (LLM) research papers updated daily, providing the latest AI research findings for researchers and developers。
Odyssey is a framework that empowers LLM-based Minecraft agents with open-world skills, featuring 40 primitive skills and 183 compositional skills, enabling AI to autonomously explore, learn, and execute diverse tasks in the Minecraft universe.
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