Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms
Jiaming Song, Linqi Zhou
2025-03-12
Summary
This paper talks about improving how AI learns to create things by focusing on making the final steps faster and better, using a method that helps AI models work smarter during the actual use phase.
What's the problem?
Current AI training methods for generating text or images are stuck using old techniques that are slow and inefficient when making final products, especially when handling mixed data like pictures and words together.
What's the solution?
The researchers suggest focusing on fixing slowdowns during the final steps of AI creation, like using their IMM method to make models produce high-quality results much faster without extra training steps.
Why it matters?
This could lead to faster, cheaper AI tools for creating art, writing, or mixed-media content, making them more practical for everyday use in apps or creative projects.
Abstract
Recent years have seen significant advancements in foundation models through generative pre-training, yet algorithmic innovation in this space has largely stagnated around autoregressive models for discrete signals and diffusion models for continuous signals. This stagnation creates a bottleneck that prevents us from fully unlocking the potential of rich multi-modal data, which in turn limits the progress on multimodal intelligence. We argue that an inference-first perspective, which prioritizes scaling efficiency during inference time across sequence length and refinement steps, can inspire novel generative pre-training algorithms. Using Inductive Moment Matching (IMM) as a concrete example, we demonstrate how addressing limitations in diffusion models' inference process through targeted modifications yields a stable, single-stage algorithm that achieves superior sample quality with over an order of magnitude greater inference efficiency.