Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems (2502.07503v4)

Abstract: Inspired by recent findings on the fractal geometry of language, we introduce Recursive INference Scaling (RINS) as a complementary, plug-in recipe for scaling inference time in language and multimodal systems. RINS is a particular form of recursive depth that significantly outperforms +55 other variants, including the recent "repeat-all-over" (RAO) strategy in Mobile LLM (Liu et al., 2024) and latent recurrent thinking (Geiping et al., 2025). Unlike prior works, we carry out our comparisons on a compute-matched regime, and demonstrate that for a fixed model size and training compute budget, RINS substantially improves LLMing performance. It also generalizes beyond pure language tasks, delivering gains in multimodal systems, including a +2% improvement in 0-shot ImageNet accuracy for SigLIP-B/16. Additionally, by deriving data scaling laws, we show that RINS improves both the asymptotic performance limits and the scaling exponents. More importantly, with light-weight (linear) adapters (comprising <1% of model parameters) and stochastic dropout, RINS offers a no-regret strategy, meaning that RINS-enabled pretraining improves performance in LLMing even when recursive depth is not applied at inference time. This corresponds to improving performance on a training compute-, parameter-, and inference-matched regime, suggesting its potential as a viable component of LLM pretraining!

• The paper introduces Recursive Inference Scaling (RINS), a novel method improving language and multimodal AI performance by leveraging the fractal geometry of language during inference.
• RINS achieves notable performance gains, including a +2% zero-shot ImageNet accuracy boost for multimodal models, without increasing model size or training compute.
• The findings suggest recursive inference scaling is a critical, underexplored path for enhancing AI efficiency and accuracy, offering benefits alongside traditional training compute scaling.

Insight into Recursive Inference Scaling for LLMs

The paper "Harnessing Language's Fractal Geometry with Recursive Inference Scaling" by Ibrahim Alabdulmohsin and Xiaohua Zhai from Google DeepMind introduces Recursive INference Scaling (RINS) as a method to enhance the performance of LLMs (LMs). This approach addresses the critical role of scaling inference compute, traditionally overshadowed by the focus on scaling training compute. The authors provide a comprehensive paper that explores RINS as a potent tool to improve both monomodal and multimodal AI systems, such as language and vision-language tasks, by leveraging the self-similar geometry of language.

Central to this research is the observation that language exhibits fractal properties, where similar patterns repeat at multiple scales of representation from words to sentences and texts. The introduction of RINS capitalizes on these self-similar structures through a recursive architecture designed to optimize LLM performance without altering model architecture size or training compute budget.

Key Findings and Technical Contributions

1. Performance Gains of RINS: The authors demonstrate that RINS significantly outperforms traditional architectures in various LLMing scenarios. Specifically, a notable improvement of +2% in zero-shot ImageNet accuracy is seen when adapting RINS to multimodal systems, such as SigLIP-B/16. This showcases the efficacy of recursively applying early layers of a network to its own output during inference.
2. Scaling Laws and Performance Metrics: Using empirical data, the paper shows RINS not only enhances the convergence speed but also improves the ultimate performance limits of LLMs. The derived scaling laws reveal that recursive inference scaling influences both the scaling exponents and asymptotic performance limits favorably.
3. Architectural Variants and Efficiency: The research methodically explores various parameter-sharing strategies and recursive depth configurations. RINS's superior performance is consistent across small to large-scale models, revealing an intrinsic advantage aligned with language's fractal nature. The paper insists that despite maintaining the same parameter count and training compute budget, RINS provides a marked enhancement in model accuracy and efficacy.
4. Stochastic Recursive Variant: A stochastic version of RINS with binomial sampling of recursion rounds during training is introduced, which further boosts model performance. This implementation provides flexibility to potentially reduce inference computation at test time with minimal performance drop-off, proving beneficial across multimodal applications.
5. Comparative Analysis with Other Domains: Unlike language, vision tasks did not benefit from recursive inference scaling, reinforcing the hypothesis that linguistic self-similarity is the underlying reason for RINS's success. Contrastive multimodal systems processing language, however, do reap substantial rewards, reaffirming language's unique structural characteristics.

Implications and Future Directions

The results of this paper open up new avenues for considering recursive structures within LLM architectures, urging a reevaluation of inference scaling across AI research domains. The robust findings emphasize the need to explore recursive inference as a complementary avenue—parallel to training compute scaling—potentially guiding new model designs for greater accuracy without necessitating increased parameter counts or extensive compute resources.

Future works could investigate the theoretical underpinnings of language's fractal properties further, seeking to rigorously define the architectural choices inherent in RINS. Extending such explorations to other structured data domains could also provide intriguing results, possibly inspiring novel strategies for inference scaling in various AI applications. Furthermore, combining RINS with different nascent prompt-based strategies might yield synergies to bolster AI’s capabilities in more complex reasoning and decision-making tasks.