Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms (2503.07154v2)

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.

Inference-time Scaling in Generative Pre-training: An Analysis

The paper "Ideas in Inference-time Scaling can Benefit Generative Pre-training Algorithms" by Jiaming Song and Linqi Zhou revisits the landscape of generative pre-training algorithms, addressing stagnation in algorithmic innovation post the rise of autoregressive models and diffusion models. The authors take an inference-first perspective, aiming to alleviate the bottleneck in scaling efficiency during inference in both discrete and continuous domains. They propose Inductive Moment Matching (IMM) as a driving example to enhance the inference process of diffusion models, enabling a stable, efficient, and high-quality generative algorithm.

Conceptual Framework

The research posits that scaling efficiency during inference is a potential source of innovations for generative pre-training algorithms. The authors identify two principal axes of inference-time scaling:

1. Sequence Length Scaling: Pertains to incrementing the number of tokens processed in models like LLMs to enhance capabilities such as reasoning and understanding long dependencies.
2. Refinement Step Scaling: Concerns iterative processes that refine model outputs, crucial for methods like diffusion models where iterative denoising lessens discretization errors and enhances generative fidelity.

Inductive Moment Matching (IMM) and Diffusion Models

Through the introduction of IMM, the authors critically assess the typical diffusion models' limitations, particularly the shortcomings of the denoising diffusion probabilistic model (DDPM) sampler in terms of its inherent reliance on denoising steps. Here, the paper presents a modification: enhancing the DDIM sampler with a three-parameter input, making it aware of the target timestep and allowing more effective one-step prediction. This modification not only offers an efficiency leap but challenges core principles rooted in denoising score matching and stochastic differential equations.

Implications and Future Directions

The potential ramifications of this work include more efficient model architectures that can asymptotically approximate the target distribution without stringent regularization steps, thus improving latency and performance without compromising sample quality. Experiments with IMM indicate promise, raising the possibility of outperforming existing diffusion paradigms both in quality and computational efficiency.

For future work, embracing this inference-centric perspective could spearhead the quest towards truly multi-modal generative models adept at handling high-dimensional, mixed-modality data at scale. It also suggests a further blending of structural ideas from both autoregressive and diffusion domains, potentially mending the schism through blockwise parallel decoding or autoregressive distribution smoothing methods.

Conclusions

Overall, the research encourages a shift from conventional algorithm development strategies that focus primarily on training to those that reconsider inference efficiency as a primary design goal. The success of Inductive Moment Matching signals that such approaches can not only address existing deficiencies but also inspire new classes of algorithms, possibly reshaping the paradigms of generative pre-training in artificial intelligence.