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Video World Models with RLVR

Method Overview

video world model

Installation

conda create -n vid_wm python==3.10
conda activate vid_wm
cd verl
pip install -e ".[vllm,gpu]"
cd ..
pip install -r requirements.txt

❗ Please make sure that vllm==0.6.3 is installed, as we do NOT implement interactive rollout for other versions.

Data Preparation

Download the RT1 dataset from Open X-Embodiment and extract single episodes as .npz files:

python oxe_data_converter.py --dataset_name fractal20220817_data --input_path {path to downloaded OXE} --output_path {path to stored npz}

Pre-training

Single-step prediction:

cd ivideogpt
bash scripts/train_perframe_tokenizer.sh --dataset_path {path to preprocessed data}
cd ivideogpt
bash scripts/train_single_step_prediction.sh --dataset_path {path to preprocessed data}

Multi-step prediction:

cd ivideogpt
bash scripts/train_compressive_tokenizer.sh --dataset_path {path to preprocessed data}
cd ivideogpt
bash scripts/train_multi_step_prediction.sh --dataset_path {path to preprocessed data}

Post-training with RLVR

Single-step prediction:

cd verl
bash examples/grpo_trainer/run_vgpt.sh \
    trainer.experiment_name='vgpt'\
    processor.processor_type=simple \
    data.video.dataset_path={path to preprocessed data} \
    processor.tokenizer.path={path to pretrained perframe tokenizer} \
    actor_rollout_ref.model.path={path to pretrained single-step pred transformer} \
    data.max_response_length=321 \
    trainer.val_before_train=True trainer.test_freq=10 trainer.save_freq=10 \
    actor_rollout_ref.rollout.n=16

Multi-step prediction:

cd verl
bash examples/grpo_trainer/run_ctx_msp_vgpt.sh \
    trainer.experiment_name='ctx_vgpt_msp8' \
    trainer.reward_fn=mae \
    data.video.dataset_path={path to preprocessed data} \
    processor.tokenizer.path={path to pretrained compressive tokenizer} \
    actor_rollout_ref.model.path={path to pretrained multi-step pred transformer} \
    trainer.val_before_train=True trainer.test_freq=10 trainer.save_freq=10 \
    actor_rollout_ref.rollout.n=16

After that, you will need the following script to merge the sharded checkpoints and generate an unwrapped model checkpoint in the directory merged_ckpt:

cd verl
python merge_sharded_ckpts.py \
    --ckpt_path {path to sharded checkpoints that ends with 'global_step_%d/actor'} \
    --config_path {**absolute** path to base model}

Video Prediction Evaluation

cd ivideogpt
bash scripts/eval_single_step_prediction.sh --dataset_path {path to preprocessed data}
bash scripts/eval_multi_step_prediction.sh --dataset_path {path to preprocessed data}

Downstream Task: Real2Sim Policy Evaluation

Follow the instructions in simpler-env to download RT-1 checkpoints.

cd ivideogpt
bash scripts/eval_policy.sh \
    --task_instruction "open middle drawer" \
    --policy_model_path pretrained_models/rt_1_tf_trained_for_000400120 \
    --dataset_path {path to preprocessed data}

Tips

  • If you encouter ImportError: cannot import name 'cached_download' from 'huggingface_hub' (/home/your_username/anaconda3/envs/your_env/lib/python3.10/site-packages/huggingface_hub/__init__.py) due to version incompatibility, just remove the import of cached_download.

Acknowledgement

Our verl codebase is forked from commit 15263cb of official repo.
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