license: apache-2.0
library_name: diffusers
pipeline_tag: robotics
tags:
- robotics
- world-model
- video-generation
- vision-action
- next-forcing
- robotwin
Next Forcing — RoboTwin Post-trained Checkpoint
Post-trained checkpoint for Next Forcing: Causal World Modeling with Multi-Chunk Prediction, evaluated on the RoboTwin 2.0 benchmark.
- 📄 Paper: https://arxiv.org/pdf/2606.11187
- 🌐 Project page: https://gangweix.github.io/next-forcing/
- 💻 Code: https://github.com/gangweix/next-forcing
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 checkpoint is the RoboTwin post-trained model, built on top of the
LingBot-VA codebase. It was post-trained
from gangweix/next-forcing-base.
| Parameters | 6.7B (BF16) |
| Backbone layers | 30 |
| MCP depths | 3 (mcp_blocks_per_depth=3, collect layers [3, 11, 19, 29]) |
| Benchmark | RoboTwin 2.0, 50 bimanual manipulation tasks |
| Initialized from | next-forcing-base (5.1B) |
Results
Average success rate on RoboTwin 2.0:
| Setting | LingBot-VA | Next Forcing |
|---|---|---|
| Clean | 92.9 | 94.1 |
| Random | 91.5 | 93.5 |
Repository Layout
transformer/ Next Forcing backbone with MCP modules (enable_mcp=true)
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-posttrain-robotwin \
--local-dir ./checkpoints/next-forcing-posttrain-robotwin
The evaluation code resolves model subfolders by path, so point
NEXT_FORCING_MODEL_PATH at the local directory, not at the Hub repository
id:
export NEXT_FORCING_MODEL_PATH=$PWD/checkpoints/next-forcing-posttrain-robotwin
export ROBOTWIN_ROOT=/path/to/your/RoboTwin
# Start the inference server on one GPU
CUDA_VISIBLE_DEVICES=0 bash evaluation/robotwin/launch_server.sh
# In another terminal, evaluate one task for 100 trials
bash evaluation/robotwin/launch_client.sh /path/to/eval_results adjust_bottle
RoboTwin evaluation requires a working RoboTwin 2.0 installation; see the official guide.
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}
}