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Orchard is an open-source framework for scalable agentic modeling that enables training diverse autonomous agents, achieving state-of-the-art results on coding, GUI navigation, and personal assistance tasks.
EasyVideoR1 is an efficient reinforcement learning framework for training large vision-language models on video understanding tasks, featuring offline preprocessing with tensor caching for 1.47x throughput improvement, a task-aware reward system covering 11 problem types, and evaluation across 22 video benchmarks. It also supports joint image-video training and a mixed offline-online data training paradigm.
Agent Lightning introduces a flexible reinforcement learning framework for training large language models in AI agents, achieving decoupling between agent execution and training to handle complex interactions.
OpenAI releases RL-Teacher, an open-source tool for training AI systems through human feedback instead of hand-crafted reward functions, with applications to safe AI development and complex reinforcement learning problems.
This article provides a detailed introduction to the basic concepts and terminology of model fine-tuning, including key knowledge points such as pre-training, supervised fine-tuning, and LoRA, suitable for beginners to understand.