New Perspectives for Generative AI Models: Increasing Efficiency in the Inference Phase

The development of generative AI models has made enormous progress in recent years. Foundation models, trained through generative pre-training, have fundamentally changed the landscape of Artificial Intelligence. Despite this impressive development, algorithmic innovation in this area is stagnating. Autoregressive models for discrete signals and diffusion models for continuous signals dominate the scene. This stagnation represents an obstacle that prevents us from exploiting the full potential of rich multimodal data and thus limits the progress of multimodal intelligence.

A promising approach to overcoming this challenge lies in prioritizing inference efficiency. Instead of primarily focusing on training, scaling efficiency during inference time, both in terms of sequence length and refinement steps, comes to the forefront. This "inference-first" perspective can inspire the development of novel generative pre-training algorithms and open the door to more efficient and powerful AI systems.

Inductive Moment Matching (IMM): An Example of Efficient Inference

A concrete example of this approach is Inductive Moment Matching (IMM). This method addresses the limitations of diffusion models in the inference phase through targeted modifications. IMM enables the development of a stable, single-step algorithm that achieves higher sample quality while significantly increasing inference efficiency. Compared to conventional diffusion models, IMM can improve inference speed by more than an order of magnitude. This illustrates the potential of the "inference-first" perspective for the development of future generative models.

Challenges and Opportunities of Inference Scaling

However, scaling inference also presents challenges. The complexity of generative models requires innovative solutions to minimize computational costs and memory requirements during the inference phase. Various approaches are conceivable here, such as optimizing the model architecture, using more efficient data structures, and developing new algorithms for parallelizing inference.

The research and development of new inference methods offers enormous opportunities for the future of generative AI. More efficient inference enables the use of more complex models and opens up new application possibilities in areas such as image generation, natural language processing, and robotics. In addition, more resource-efficient inference methods can help reduce the energy consumption of AI systems and thus promote the sustainability of AI technologies.

Outlook

The "inference-first" perspective opens a wider design spectrum for future generative models. By placing efficiency in the inference phase at the center of development, we can overcome the limitations of existing algorithms and exploit the full potential of generative AI. Research in this area is crucial to develop the next generation of AI models that are both powerful and efficient, thus contributing to a sustainable and responsible use of AI technologies.

Bibliography: - https://arxiv.org/abs/2503.07154 - https://arxiv.org/pdf/2503.07154 - https://huggingface.co/papers/2503.07154 - http://paperreading.club/page?id=290426 - https://lumalabs.ai/news/inductive-moment-matching - https://github.com/ThreeSR/Awesome-Inference-Time-Scaling - https://chatpaper.com/chatpaper/fr?id=5&date=1741622400&page=1 - https://www.researchgate.net/publication/382739350_Large_Language_Monkeys_Scaling_Inference_Compute_with_Repeated_Sampling - https://huggingface.co/papers?q=scaling%20behaviors