Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems

• URL: http://arxiv.org/abs/2502.07503v4
• Date: Thu, 08 May 2025 11:40:01 GMT
• Title: Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems
• Authors: Ibrahim Alabdulmohsin, Xiaohua Zhai,
• Abstract summary: We introduce Recursive INference Scaling (RINS) as a complementary, plug-in recipe for scaling inference time in language and multimodal systems.<n>RINS significantly outperforms +55 other variants, including the recent "repeat-all-over" (RAO) strategy in Mobile LLM.<n>With light-weight adapters, RINS offers a no-regret strategy, meaning that RINS-enabled pretraining improves performance in language modeling.
• Score: 21.01887711305712
• License: http://creativecommons.org/licenses/by/4.0/
• 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 language modeling 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 language modeling 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!

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