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RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
This project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u
The code for BRIDGE (Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning).
LUFFY: Learning to Reason Under Off‑Policy Guidance A general framework for off-policy learning in large reasoning models.
The main features of elliottyan/luffy are: Off-Policy Optimization.
Projects with overlapping indexed features include: morvanzhou/pytorch-tutorial — This project is a collection of PyTorch learning resources and educational guides designed to teach the construction… rlinf/rlinf — RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the… chanliang/bridge — The code for BRIDGE (Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning). liumy2010/uft — Mingyang Liu, Gabriele Farina, Asuman Ozdaglar. millioniron/openrlhf-millioniron- — Open-source / Comprehensive / Lightweight / Easy-to-use. mozerwang/ampo — Minzheng Wang 1,2 , Yongbin Li 3 , Haobo Wang 4 , Xinghua Zhang 3🌟 , Nan Xu 1 , Bingli Wu 3 , Fei Huang 3 , Haiyang…
