Multimodal LLM Training

Design and implement a training pipeline for a multimodal large language model (LLM) that ingests image–text pairs, audio snippets, and short video clips and learns to answer open-ended questions about any combination of these modalities. Your solution must support four training stages: (1) separate pre-training of vision, audio, and text encoders; (2) cross-modal alignment via contrastive learning; (3) instruction fine-tuning on multimodal QA samples; and (4) RLHF for human preference alignment. You should describe the model architecture (encoder fusion vs. unified tokenizer), the data curation and filtering strategy, the distributed training setup (parameter counts, GPU topology, checkpointing), and the evaluation protocol (retrieval recall, VQA accuracy, human win-rate). Provide pseudocode for the forward pass, the contrastive loss, and the RLHF reward modeling step. Discuss how you would handle scale: 2 B image–text pairs, 200 M audio–text pairs, 50 M video–text triplets, with a 7 B-parameter transformer backbone, on a 512-GPU cluster with 80 GB A100s.