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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.
Individual comparisons by ranking methods
Wilcoxon, F. (1992) · 1992
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Scaling laws for neural language models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020) · 2001
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009) · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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Predicting sample size required for classification performance
Figueroa, R. L., Zeng-Treitler, Q., Kandula, S., and Ngo, L. H. (2012) · 2012
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V. (2012) · 2012
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Sample size planning for classification models
Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., and Popp, J. (2013) · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Young, P., Lai, A., Hodosh, M., and Hockenmaier, J. (2014) · 2014
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Microsoft coco captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L. (2015) · 2015
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Cho, J., Lee, K., Shin, E., Choy, G., and Do, S. (2015) · 2015
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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. (2016) · 2016
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Deep learning scaling is predictable, empirically
Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y. (2017) · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I. (2017) · 2017
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A. (2018) · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2018) · 2018
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K. (2019) · 2019
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Universal transformers
Dehghani, M., Gouws, S., Vinyals, O., Uszkoreit, J., and Łukasz Kaiser (2019) · 2019
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Socialiqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., LeBras, R., and Choi, Y. (2019) · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J. (2019) · 2019
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Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y. (2019) · 2019
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Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Bras, R. L., Gao, J., and Choi, Y. (2020) · 2020
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Albert: A lite bert for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R. (2020) · 2020
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Comparing fast thinking and slow thinking: The relative benefits of interventions, individual differences, and inferential rules
Scaling laws from the data manifold dimension
Sharma, U. and Kaplan, J. (2022) · 2022
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Crossmodal-3600: A massively multilingual multimodal evaluation dataset
Thapliyal, A. V., Pont-Tuset, J., Chen, X., and Soricut, R. (2022) · 2022
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Scaling vision transformers
Zhai, X., Kolesnikov, A., Houlsby, N., and Beyer, L. (2022) · 2022
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SlimPajama: A 627B token cleaned and deduplicated version of RedPajama
Soboleva, D., Al-Khateeb, F., Myers, R., Steeves, J. R., Hestness, J., and Dey, N. (2023) · 2023
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D. (2023) · 2023
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React: Synergizing reasoning and acting in language models
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Lawson, M. A., Larrick, R. P., and Soll, J. B. (2020) · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J. (2020) · 2020
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Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval
Weyand, T., Araujo, A., Cao, B., and Sim, J. (2020) · 2020
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Explaining neural scaling laws
Bahri, Y., Dyer, E., Kaplan, J., Lee, J., and Sharma, U. (2021) · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W. (2021) · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. (2021) · 2021
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Hutter, M. (2021) · 2021
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Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. (2023) · 2023
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Sigmoid loss for language image pre-training
Zhai, X., Mustafa, B., Kolesnikov, A., and Beyer, L. (2023) · 2023
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Critique-out-loud reward models
Ankner, Z., Paul, M., Cui, B., Chang, J. D., and Ammanabrolu, P. (2024) · 2024
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Large language monkeys: Scaling inference compute with repeated sampling
Brown, B., Juravsky, J., Ehrlich, R., Clark, R., Le, Q. V., Ré, C., and Mirhoseini, A. (2024) · 2024
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Are more llm calls all you need? towards the scaling properties of compound ai systems
Chen, L., Davis, J. Q., Hanin, B., Bailis, P., Stoica, I., Zaharia, M., and Zou, J. (2024) · 2024
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Mobilellm: Optimizing sub-billion parameter language models for on-device use cases
Liu, Z., Zhao, C., Iandola, F., Lai, C., Tian, Y., Fedorov, I., Xiong, Y., Chang, E., Shi, Y., Krishnamoorthi, R., et al. (2024) · 2024
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Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. (2024) · 2024
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Gemma: Open models based on gemini research and technology
Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., Tafti, P., Hussenot, L., Sessa, P. G., Chowdhery, A., Roberts, A., Barua, A., Botev, A., Castro-Ros, A., Slone, A., Héliou, A., Tacchetti, A., Bulanova, A., Paterson, A., Tsai, B., Shahriari, B., Lan, C. L., Choquette-Choo, C. A., Crepy, C., Cer, D., Ippolito, D., Reid, D., Buchatskaya, E., Ni, E., Noland, E., Yan, G., Tucker, G., Muraru, G.-C., Rozhdestvenskiy, G., Michalewski, H., Tenney, I., Grishchenko, I., Austin, J., Keeling, J., Labanowski, J., Lespiau, J.-B., Stanway, J., Brennan, J., Chen, J., Ferret, J., Chiu, J., Mao-Jones, J., Lee, K., Yu, K., Millican, K., Sjoesund, L. L., Lee, L., Dixon, L., Reid, M., Mikuła, M., Wirth, M., Sharman, M., Chinaev, N., Thain, N., Bachem, O., Chang, O., Wahltinez, O., Bailey, P., Michel, P., Yotov, P., Chaabouni, R., Comanescu, R., Jana, R., Anil, R., McIlroy, R., Liu, R., Mullins, R., Smith, S. L., Borgeaud, S., Girgin, S., Douglas, S., Pandya, S., Shakeri, S., De, S., Klimenko, T., Hennigan, T., Feinberg, V., Stokowiec, W., hui Chen, Y., Ahmed, Z., Gong, Z., Warkentin, T., Peran, L., Giang, M., Farabet, C., Vinyals, O., Dean, J., Kavukcuoglu, K., Hassabis, D., Ghahramani, Z., Eck, D., Barral, J., Pereira, F., Collins, E., Joulin, A., Fiedel, N., Senter, E., Andreev, A., and Kenealy, K. (2024) · 2024
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No filter: Cultural and socioeconomic diversity in contrastive vision-language models
Pouget, A., Beyer, L., Bugliarello, E., Wang, X., Steiner, A. P., Zhai, X., and Alabdulmohsin, I. (2024) · 2024
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Geode: a geographically diverse evaluation dataset for object recognition
Ramaswamy, V. V., Lin, S. Y., Zhao, D., Adcock, A., van der Maaten, L., Ghadiyaram, D., and Russakovsky, O. (2024) · 2024
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. (2024) · 2024
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From decoding to meta-generation: Inference-time algorithms for large language models
Welleck, S., Bertsch, A., Finlayson, M., Schoelkopf, H., Xie, A., Neubig, G., Kulikov, I., and Harchaoui, Z. (2024) · 2024
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Scaling up test-time compute with latent reasoning: A recurrent depth approach
Geiping, J., McLeish, S., Jain, N., Kirchenbauer, J., Singh, S., Bartoldson, B. R., Kailkhura, B., Bhatele, A., and Goldstein, T. (2025) · 2025
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