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.

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).

Built on top of the LingBot-VA codebase.

Clone the code and install the dependencies as described in the repository README.

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:

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].

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.