Next Forcing — Base Checkpoint
Base checkpoint for Next Forcing: Causal World Modeling with Multi-Chunk Prediction. This is the causal video-action backbone used as the initialization for post-training.
- 📄 Paper: https://arxiv.org/pdf/2606.11187
- 🌐 Project page: https://gangweix.github.io/next-forcing/
- 💻 Code: https://github.com/gangweix/next-forcing
- 🤖 RoboTwin post-trained model:
gangweix/next-forcing-posttrain-robotwin
Model Description
Next Forcing addresses the myopic supervision problem in autoregressive video world models: next-chunk denoising tends to learn local appearance shortcuts instead of long-range dynamics, especially at high frame rates. Lightweight Multi-Chunk Prediction (MCP) modules predict multiple future chunks through a causal chain during training, providing dense temporal supervision to the backbone.
This base checkpoint contains the backbone without MCP modules. The MCP
modules are created at the start of post-training and initialized from the last
mcp_blocks_per_depth backbone blocks (mcp_init_from_backbone = True), so the
post-trained checkpoint is correspondingly larger (6.7B vs 5.1B).
| Parameters | 5.1B (BF16) |
| Backbone layers | 30 |
| MCP modules | Not included (added during post-training) |
Built on top of the LingBot-VA codebase.
Repository Layout
transformer/ Causal video-action backbone
vae/
text_encoder/
tokenizer/
Usage
Clone the code and install the dependencies as described in the repository README.
python -m pip install "huggingface_hub[cli]"
hf download gangweix/next-forcing-base \
--local-dir ./checkpoints/next-forcing-base
The training code resolves model subfolders by path, so point
NEXT_FORCING_PRETRAINED_MODEL_PATH at the local directory, not at the Hub
repository id:
export NEXT_FORCING_PRETRAINED_MODEL_PATH=$PWD/checkpoints/next-forcing-base
export NEXT_FORCING_DATASET_PATH=/path/to/your/dataset
export NEXT_FORCING_SAVE_ROOT=/path/to/your/output
NGPU=8 CONFIG_NAME=robotwin_train \
bash script/run_va_posttrain.sh --init-worker 1
MCP defaults (wan_va/configs/mcp_train_config.py): num_mcp_depths = 3,
mcp_blocks_per_depth = 3, mcp_hidden_collect_layers = [3, 11, 19, 29],
mcp_loss_weights = [0.5, 0.2, 0.1].
License
Released under the Apache License 2.0. Next Forcing is developed on top of the LingBot-VA codebase; please retain the upstream attribution and license when redistributing.
Citation
@article{xu2026next,
title={Next Forcing: Causal World Modeling with Multi-Chunk Prediction},
author={Xu, Gangwei and Zhang, Qihang and Zhou, Jiaming and Zhu, Xing and Shen, Yujun and Yang, Xin and Xu, Yinghao},
journal={arXiv preprint arXiv:2606.11187},
year={2026}
}