近期，人工智能领域迎来了一项激动人心的创新——MIT CSAIL的研究团队，由在读博士陈博远作为主要作者，成功融合了全序列扩散模型与下一token预测模型的强大力量，推出了一种革新性的训练和采样范式——Diffusion Forcing（DF）。这一突破性的成果，不仅在一致性和稳定性方面超越了当前的全序列扩散和教师强制两种方法，更在无限生成视频、规划决策等复杂任务中展现出了强大的潜力，为人工智能的数字化转型和前沿科技发展注入了新的活力。
#### **DF框架的创新之处**
DF框架的核心在于其独特的训练和采样策略。每个输入的token都关联了一个随机、独立的噪声水平，这一特性使得模型能够灵活地适应不同长度的序列生成任务。通过共享或下几token预测模型，DF允许根据任意、独立的每token方案对token进行去噪。这一创新性策略基于一个关键观察：对token加噪声的过程本质上是一种部分掩码过程，无噪声意味着未对token进行掩码，而完整噪声则完全掩盖了token。因此，DF能够强制模型学习去除任何可变噪声水平集合的掩码，从而实现对序列中任意token的精准控制。
#### **Causal Diffusion Forcing（CDF）的实现与优势**
CDF是DF框架在序列生成领域的具体实现，其采用因果架构，使得未来token依赖于过去token，确保了生成序列的连贯性和逻辑性。CDF通过一次性去噪序列的所有token（每个token具有独立的噪声水平）进行训练，显著提高了生成序列的稳定性和多样性。在采样阶段，CDF能够通过逐步去噪一个高斯噪声帧序列，生成洁净样本，不同帧在每个去噪步骤中可能有不同的噪声水平，这一特性使得CDF在生成长度可变的序列时表现出色，同时保证了生成过程的稳定性和高奖励生成的能力。
#### **MCTG：提升高奖励生成的创新策略**
CDF还引入了蒙特卡洛树引导（MCTG）策略，相较于非因果全序列扩散模型，MCTG能够极大地提升高奖励生成的采样率。这一策略通过协同利用因果关系、灵活的范围和可变噪声调度，为复杂决策和优化问题提供了高效的解决方案，进一步拓展了DF框架在AI领域的应用边界。
### 结论
Diffusion Forcing（DF）及其在序列生成领域中的实现——Causal Diffusion Forcing（CDF）与MCTG策略，为人工智能领域带来了全新的范式。这一创新不仅在一致性、稳定性、灵活性和高奖励生成方面展现出了显著优势，更在无限生成视频、规划决策等复杂任务中展现出强大的应用潜力，为未来的AI技术发展开辟了新的方向。这一研究成果的发布，不仅标志着AI技术的又一次重要突破，也为相关领域的研究者和开发者提供了新的灵感和工具，有望在未来推动AI技术在更广泛领域的应用与创新。
英语如下：
### Revolutionary Integration: MIT Team’s Breakthrough in AI with Diffusion Forcing and Next-Token Prediction
In a significant advance in the AI field, the CSAIL research team from MIT, led by doctoral candidate Bo Yuan Chen as the principal author, has innovatively combined the powerful forces of full-sequence diffusion models and next-token prediction models. This pioneering approach, named Diffusion Forcing (DF), introduces a novel training and sampling paradigm that not only outperforms current full-sequence diffusion and teacher-forcing methods in terms of consistency and stability but also demonstrates immense potential in complex tasks such as infinite video generation and decision-making planning. This innovation injects new vitality into the digital transformation of AI and the development of cutting-edge technologies.
#### **The Innovative Aspect of the DF Framework**
The core of the DF framework lies in its unique training and sampling strategy. Each input token is associated with a random, independent noise level, enabling the model to flexibly adapt to tasks of varying sequence lengths. Through the sharing or prediction of the next few tokens, DF allows for the denoising of tokens according to any independent scheme for each token. This innovative strategy is based on the key observation that the process of adding noise to a token is fundamentally a partial masking process. A lack of noise means that the token is not masked, while complete noise fully conceals the token. Consequently, DF forces the model to learn to remove any set of noise levels, achieving precise control over any token within the sequence.
#### **Causal Diffusion Forcing (CDF): Implementation and Advantages**
Causal Diffusion Forcing (CDF) is the specific implementation of the DF framework in the domain of sequence generation. It adopts a causal architecture, ensuring that future tokens depend on past tokens, thus maintaining the coherence and logical consistency of generated sequences. CDF is trained by denoising the entire sequence of tokens (each token with its own independent noise level), which significantly enhances the stability and diversity of the generated sequences. During the sampling phase, CDF is capable of generating clean samples by gradually denoising a Gaussian noise frame sequence, with different frames having different noise levels at each denoising step. This characteristic makes CDF excel in generating sequences of varying lengths while maintaining the stability and high reward generation capability of the process.
#### **MCTG: Innovative Strategy for High-Reward Sampling**
CDF also introduces the Monte Carlo Tree Guiding (MCTG) strategy, which significantly boosts the sampling rate for high-reward generation compared to non-causal full-sequence diffusion models. This strategy, through the synergistic use of causality, flexibility, and variable noise scheduling, provides an efficient solution for complex decision-making and optimization problems, further expanding the application boundaries of the DF framework in the AI domain.
### Conclusion
Diffusion Forcing (DF) and its realization in the field of sequence generation, Causal Diffusion Forcing (CDF) with the MCTG strategy, have introduced a new paradigm to the AI domain. This innovation showcases significant advantages in consistency, stability, flexibility, and high-reward generation, and demonstrates immense potential in complex tasks such as infinite video generation and planning decision-making. This research breakthrough not only marks another significant leap in AI technology but also offers new inspiration and tools for researchers and developers in related fields, promising to propel the application and innovation of AI technology in a broader range of domains in the future.
【来源】https://www.jiqizhixin.com/articles/2024-07-23-2
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