This repository implements a complete reinforcement learning pipeline for whole-body motion tracking on humanoid robots, specifically Unitree G1 and XBot L7. The system uses a teacher-student architecture with Proximal Policy Optimization (PPO) to train policies that track reference motions from retargeted motion capture datasets. It features three training phases: a base training phase, an adaptation phase with domain randomization, and a finetuning phase. The environment is built on MJLab (a MuJoCo wrapper) and includes extensive domain randomization (e.g., motor parameters, body mass, center of mass, joint offsets, external forces, and a window load capacity curriculum for payload-aware training). The codebase is highly modular, with registered MDP components (observations, rewards, terminations, randomizations) and a flexible configuration system using Hydra. It also includes advanced features such as symmetry augmentation, differentiable forward kinematics, and a custom motion dataset pipeline with memory-mapped storage. While primarily a reinforcement learning training framework, it has strong data engineering aspects for processing motion datasets and generating training data for the student encoder. The repository is well-structured for research and sim-to-real transfer, but it is tightly coupled to specific robot assets and dependencies (torch, torchrl, mjlab) with pinned versions that may require adaptation for other environments.