MiniWorld: Democratizing the Training of Video World Models from Scratch

• URL: http://arxiv.org/abs/2608.01127v2
• Date: Tue, 04 Aug 2026 04:32:45 GMT
• Title: MiniWorld: Democratizing the Training of Video World Models from Scratch
• Abstract summary: Video world models predict future observations conditioned on historical observations and control signals.<n>MiniWorld is a reproducible framework for training streaming video world models from scratch.
• Score: 13.334555097211636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video world models predict future observations conditioned on historical observations and control signals, enabling long-horizon generation through autoregressive state transitions. Unlike conventional video generation models that primarily capture visual appearance and motion, video world models learn the underlying dynamics governing environment evolution under agent actions, providing a foundation for embodied AI and interactive simulation. Recent progress has largely relied on adapting pretrained video generation models through post-training or distillation. Although effective, these approaches often require complex training pipelines, substantial computational resources, and suffer from the mismatch between bidirectional pretraining and causal streaming inference. Recent studies have shown that training autoregressive video world models from scratch is feasible and scalable. However, the community still lacks a lightweight, transparent, and fully reproducible baseline trainable end-to-end with modest computational resources. We present MiniWorld, a reproducible framework for training streaming video world models from scratch. MiniWorld employs a block-causal Video Diffusion Transformer trained with Flow Matching in the latent space of a pretrained Video VAE. Building on Diffusion Forcing, it adopts a chunk-wise non-decreasing noise schedule and two-stage continued training to improve temporal modeling and stability. During inference, MiniWorld combines a rolling KV cache with pipelined asynchronous denoising for efficient streaming generation under bounded computation. The entire model can be trained within several days on a single 8-GPU server. By releasing the training and inference codebase and pretrained checkpoints, we hope MiniWorld will facilitate future research on video world modeling.

Related papers

• minWM: A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models [51.45338589543413]
  minWM is a full-stack open-source framework for building real-time interactive video world models.<n> minWM converts existing bidirectional T2V/TI2V video foundation models into camera-controllable few-step autoregressive world models.
  arXiv  Detail & Related papers  (2026-05-28T17:27:03Z)
• Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory [53.39687409541093]
  Matrix-Game 3.0 is a memory-augmented interactive world model designed for 720p real-time longform video generation.<n>We introduce systematic improvements across data, model, and inference.<n> Experimental results show that Matrix-Game 3.0 achieves up to 40 FPS real-time generation at 720p resolution with a 5B model.
  arXiv  Detail & Related papers  (2026-04-10T06:00:09Z)
• Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining [28.30092786035367]
  DeFI is a novel framework that Decouples visual Forward and Inverse dynamics pretraining to exploit respective data sources.<n>We introduce the General Forward Dynamics Model (GFDM), pretrained on diverse human and robot videos for future prediction, and the General Inverse Dynamics Model (GIDM), trained via self-supervised learning to infer latent actions from unlabeled video transitions.<n>Experiments on CALVIN ABC-D and SimplerEnv demonstrate state-of-the-art performance, with DeFI achieving an average task length of 4.51 for CALVIN, 51.2% success rate on SimplerEnv-Frac
  arXiv  Detail & Related papers  (2026-03-27T17:20:10Z)
• End-to-End Training for Autoregressive Video Diffusion via Self-Resampling [63.84672807009907]
  Autoregressive video diffusion models hold promise for world simulation but are vulnerable to exposure bias arising from the train-test mismatch.<n>We introduce Resampling Forcing, a teacher-free framework that enables training autoregressive video models from scratch and at scale.
  arXiv  Detail & Related papers  (2025-12-17T18:53:29Z)
• Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation [41.993197533574126]
  Inferix is an inference engine to enable immersive world synthesis through optimized semi-autoregressive decoding processes.<n>Inferix further enhances its offering with interactive video streaming and profiling, enabling real-time interaction and realistic simulation.
  arXiv  Detail & Related papers  (2025-11-25T01:45:04Z)
• LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model [22.92353994818742]
  Driving world models are used to simulate futures by video generation based on the condition of the current state and actions.<n>Recent studies utilize the Diffusion Transformer (DiT) as the backbone of driving world models to improve learning flexibility.<n>We propose several solutions to build a simple yet effective long-term driving world model.
  arXiv  Detail & Related papers  (2025-06-02T11:19:23Z)
• Vid2World: Crafting Video Diffusion Models to Interactive World Models [35.42362065437052]
  We present Vid2World, a general approach for leveraging and transferring pre-trained video diffusion models into interactive world models.<n>Our method offers a scalable and effective pathway for repurposing highly capable video diffusion models into interactive world models.
  arXiv  Detail & Related papers  (2025-05-20T13:41:45Z)
• iVideoGPT: Interactive VideoGPTs are Scalable World Models [70.02290687442624]
  World models empower model-based agents to interactively explore, reason, and plan within imagined environments for real-world decision-making. This work introduces Interactive VideoGPT, a scalable autoregressive transformer framework that integrates multimodal signals--visual observations, actions, and rewards--into a sequence of tokens. iVideoGPT features a novel compressive tokenization technique that efficiently discretizes high-dimensional visual observations.
  arXiv  Detail & Related papers  (2024-05-24T05:29:12Z)
• Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning [54.67880602409801]
  In this paper, we study the problem of pre-training world models with abundant in-the-wild videos for efficient learning of visual control tasks. We introduce Contextualized World Models (ContextWM) that explicitly separate context and dynamics modeling. Our experiments show that in-the-wild video pre-training equipped with ContextWM can significantly improve the sample efficiency of model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-05-29T14:29:12Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.