LLaVA-NeXT is a large multimodal model (LMM) research codebase that extends the LLaVA family to support single-image, multi-image, video, and interleaved tasks. It provides training pipelines for supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (PPO, GRPO) with custom vision encoders (CLIP, SigLIP, EVA-CLIP, MLCD) and modular projector/resampler components. The repository also includes a full RL training framework (EasyR1/verl) for large-scale GRPO training using Ray, FSDP, and vLLM, as well as evaluation scripts for video and interleaved benchmarks. While primarily a research codebase with extensive training configurations and experimental scripts, it also contains serving infrastructure (Gradio, SGLang, controller-worker) for deployment. The code is well-structured with reusable vision encoder wrappers, data protocols, and RL algorithm implementations, but suffers from hardcoded internal paths, multiple overlapping RL implementations, and legacy training pipelines that increase maintenance burden.