相互推动：双模式自我进化用于快速自回归音频视频生成
Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation
AI总结：
本文提出Mutual Forcing框架，通过双阶段训练策略解决音频视频联合建模与快速自回归生成问题，通过共享参数实现自蒸馏，提升训练推理一致性，相比传统方法更高效且质量更优。
摘要：
在本文中，我们提出Mutual Forcing，一种用于长时音频视频同步的快速自回归生成框架。我们的方法解决了两个关键挑战：联合音频视频建模和快速自回归生成。为简化联合音频视频优化，我们采用两阶段训练策略：首先训练单模生成器，然后将其耦合为统一的音频视频模型进行联合训练。对于流式生成，我们探讨是否可以直接训练原生的快速因果音频视频模型，而不是遵循现有流式蒸馏管道，通常先训练双向模型再通过多阶段蒸馏转换为因果生成器。我们的答案是Mutual Forcing，它直接建立在原生自回归模型上，并在单个权重共享模型中整合少量步和多步生成，实现自蒸馏和改进的训练-推理一致性。多步模式通过自蒸馏提升少量步模式，而少量步模式在训练中生成历史上下文以提升训练-推理一致性；由于两种模式共享参数，这两种效果在单个模型内相互增强。与先前方法如Self-Forcing相比，Mutual Forcing消除了对额外双向教师模型的需求，支持更灵活的训练序列长度，减少训练开销，并允许模型直接从真实配对数据中提升，而不是固定教师。实验表明，Mutual Forcing在使用仅4到8步时匹配或超越需要约50个采样步的强大基线，展示了在效率和质量上的显著优势。项目页面可在https://mutualforcing.github.io上找到。
英文摘要：
In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-video generation with long-horizon audio-video synchronization. Our approach addresses two key challenges: joint audio-video modeling and fast autoregressive generation. To ease joint audio-video optimization, we adopt a two-stage training strategy: we first train uni-modal generators and then couple them into a unified audio-video model for joint training on paired data. For streaming generation, we ask whether a native fast causal audio-video model can be trained directly, instead of following existing streaming distillation pipelines that typically train a bidirectional model first and then convert it into a causal generator through multiple distillation stages. Our answer is Mutual Forcing, which builds directly on native autoregressive model and integrates few-step and multi-step generation within a single weight-shared model, enabling self-distillation and improved training-inference consistency. The multi-step mode improves the few-step mode via self-distillation, while the few-step mode generates historical context during training to improve training-inference consistency; because the two modes share parameters, these two effects reinforce each other within a single model. Compared with prior approaches such as Self-Forcing, Mutual Forcing removes the need for an additional bidirectional teacher model, supports more flexible training sequence lengths, reduces training overhead, and allows the model to improve directly from real paired data rather than a fixed teacher. Experiments show that Mutual Forcing matches or surpasses strong baselines that require around 50 sampling steps while using only 4 to 8 steps, demonstrating substantial advantages in both efficiency and quality. The project page is available at https://mutualforcing.github.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
