Google DeepMind: World Models and Scalable Objectives
World Models and AGI: Technological Evolution and Future Outlook
1. The Core Role of World Models
World Models are regarded as a key technology for achieving AGI (Artificial General Intelligence), with their core lying in integrating multimodal data (such as vision, language, and physical laws) and long-term reasoning capabilities to simulate the dynamic changes of complex environments. They are not only a potential path toward AGI but are also reshaping the technical logic of fields such as robotics, video generation, and reinforcement learning.
2. Dual Wave Transformation in Robotics
- First Wave: Representation Optimization
Video prediction models learn physical details (such as object positions, friction, and fluid dynamics), providing precise physical representations for robot control and significantly enhancing control accuracy and generalization, overcoming the limitations of traditional visual models. - Second Wave: Virtual Training
Pretrained World Models combined with fine-tuning using a small amount of real data can simulate a robot’s performance in any scenario, greatly reducing the cost of actual training and enabling large-scale parallel training (e.g., virtual kitchen scenarios). This marks the second technological leap in the robotics field.
3. Challenges of Objective Functions and Unified Path
Current objective functions face two types of issues:
- Preference-based objectives (e.g., generating content aligned with human values) require extensive manual feedback for optimization;
- Information-based objectives (e.g., predicting the next frame of a video) demand deeper causal relationship modeling.
Hafner speculates that future unified objective functions may integrate multimodal learning, reducing the complexity of loss function weight balancing and enhancing model performance.
4. Division of Pretraining and Reinforcement Learning
- Pretraining: Efficiently absorbs knowledge from large-scale data, suitable for knowledge learning (e.g., language understanding);
- Reinforcement Learning: Optimizes strategies through trial and error, suitable for complex tasks (e.g., robot control).
Combining both, World Models provide a unified platform for observational learning (pretraining) and trial-and-error learning (reinforcement learning).
5. Hallucination Issues in Large Language Models
Hallucination phenomena arise from models’ failure to generalize in distributional edge regions (areas with insufficient training). Solutions include:
- Expanding model scale and data volume to cover a broader distribution;
- Introducing online reinforcement learning feedback mechanisms, reducing hallucination through user corrections (negative rewards) and enhancing model stability.
6. Prospects for Practical General-Purpose Robots
Hafner predicts that the robotics field may achieve practical general-purpose robots (e.g., household cleaning, cooking) within 3-5 years, requiring no complex reasoning capabilities but precise physical control and scene adaptation. Breakthroughs in long-term reasoning may take 5-10 years, but practical robots can be deployed first.
7. Technological Convergence and Future Directions
The maturation of World Models will drive deep integration across fields such as AGI, robotics, and video generation, accelerating technological implementation. Future efforts should focus on overcoming challenges in objective function design, virtual training transfer, and multimodal unified learning frameworks, ultimately achieving more efficient and autonomous intelligent systems.
Summary
World Models are not only a potential path toward AGI but also a core driver of current technological breakthroughs. By integrating multimodal data, optimizing robot control, and addressing hallucination issues, they are reshaping the paradigm of AI development, opening new paths for general intelligence and practical applications.
Translation
世界模型与AGI：技术演进与未来展望
1. 世界模型的核心作用
世界模型（World Model）被视作实现AGI（通用人工智能）的关键技术，其核心在于通过多模态数据整合（如视觉、语言、物理规律）和长期推理能力，模拟复杂环境的动态变化。它不仅是AGI的潜在路径，还正在重塑机器人、视频生成、强化学习等领域的技术逻辑。
2. 机器人领域的双波变革
- 第一波：表征优化
视频预测模型通过学习物理细节（如物体位置、摩擦力、液体流动等），为机器人控制提供精准的物理表征，显著提升控制精度和泛化能力，克服传统视觉模型的局限性。 - 第二波：虚拟训练
预训练的世界模型结合少量真实数据微调，可模拟机器人在任意场景中的表现，大幅降低实际训练成本，实现大规模并行训练（如虚拟厨房场景）。这标志着机器人领域的第二波技术跃迁。
3. 目标函数的挑战与统一路径
当前目标函数存在两类问题：
- 偏好型目标（如生成符合人类价值观的内容）需大量人工反馈优化；
- 信息型目标（如预测视频下一帧）需更深层的因果关系建模。
哈夫纳推测，未来可能通过统一目标函数整合多模态学习，减少损失函数权重平衡的复杂性，提升模型性能。
4. 预训练与强化学习的分工
- 预训练：高效吸收大规模数据中的知识，适合知识学习（如语言理解）；
- 强化学习：通过试错优化策略，适合复杂任务（如机器人控制）。
两者结合，世界模型为观察学习（预训练）和试错学习（强化学习）提供统一平台。
5. 大语言模型的幻觉问题
幻觉现象源于模型在分布边缘区域（训练不足的区域）的泛化失败。解决方法包括：
- 扩大模型规模和数据量以覆盖更广的分布；
- 引入在线强化学习反馈机制，通过用户纠正（负奖励）减少幻觉，提升模型稳定性。
6. 实用型通用机器人的前景
哈夫纳预测，机器人领域可能在3-5年内实现实用型通用机器人（如家庭清洁、烹饪），无需复杂推理能力，仅需精准物理控制和场景适应。长期推理能力的突破可能需要5-10年，但实用型机器人可先行落地。
7. 技术融合与未来方向
世界模型的成熟将推动AGI、机器人、视频生成等领域的深度融合，加速技术落地。未来需重点突破目标函数设计、虚拟训练迁移、以及多模态统一学习框架，最终实现更高效、自主的智能系统。
总结
世界模型不仅是AGI的潜在路径，更是当前技术突破的核心驱动力。通过整合多模态数据、优化机器人控制、解决幻觉问题，它正在重塑人工智能的发展范式，为通用智能和实际应用开辟新路径。
Reference:
https://www.youtube.com/watch?v=OzVC6pT2TBI