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Large language models are increasingly solving tasks that are commonly believed to require human-level reasoning ability.
Catastrophic forgetting in connectionist networks
French, R · 1999
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DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
Ellis, K., Wong, C., Nye, M. I., Sablé-Meyer, M., Cary, L., Morales, L., Hewitt, L. B., Solar-Lezama, A., and Tenenbaum, J. B · 2006
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Bandit based monte-carlo planning
Kocsis, L. and Szepesvári, C · 2006
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Core knowledge
Spelke, E. S. and Kinzler, K. D · 2007
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Search-based structured prediction
Daumé, H., Langford, J., and Marcu, D · 2009
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2015
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Hindsight experience replay
Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Tobin, J., Pieter Abbeel, O., and Zaremba, W · 2017
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Thinking fast and slow with deep learning and tree search
Anthony, T., Tian, Z., and Barber, D · 2017
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Deepcoder: Learning to write programs
Balog, M., Gaunt, A., Brockschmidt, M., Nowozin, S., and Tarlow, D · 2017
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Robustfill: Neural program learning under noisy i/o
Devlin, J., Uesato, J., Bhupatiraju, S., Singh, R., Mohamed, A.-r., and Kohli, P · 2017
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Neuro-symbolic program synthesis
Parisotto, E., Mohamed, A.-r., Singh, R., Li, L., Zhou, D., and Kohli, P · 2017
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Leveraging grammar and reinforcement learning for neural program synthesis
Bunel, R., Hausknecht, M., Devlin, J., Singh, R., and Kohli, P · 2018
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Neural program search: Solving programming tasks from description and examples
Polosukhin, I. and Skidanov, A · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
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Automatic program synthesis of long programs with a learned garbage collector
Zohar, A. and Wolf, L · 2018
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On the measure of intelligence
Chollet, F · 2019
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Ranked reward: enabling self-play reinforcement learning for bin packing
Laterre, A., Fu, Y., Jabri, M. K., Cohen, A.-S., Kas, D., Hajjar, K., Chen, H., Dahl, T. S., Kerkeni, A., and Beguir, K · 2019
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Kolev, V., Georgiev, B., and Penkov, S · 2020
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Learning to prove theorems by learning to generate theorems
Wang, M. and Deng, J · 2020
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A Neurosymbolic Approach to Abstraction and Reasoning
Alford, S · 2021
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Neural-guided, bidirectional program search for abstraction and reasoning
Alford, S., Gandhi, A., Rangamani, A., Banburski, A., Wang, T., Dandekar, S., Chin, J., Poggio, T. A., and Chin, P · 2021
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Proving theorems using incremental learning and hindsight experience replay, 2021
Aygün, E., Orseau, L., Anand, A., Glorot, X., Firoiu, V., Zhang, L. M., Precup, D., and Mourad, S · 2021
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Decision transformer: Reinforcement learning via sequence modeling, 2021
Rest meets react: Self-improvement for multi-step reasoning llm agent, 2023
Aksitov, R., Miryoosefi, S., Li, Z., Li, D., Babayan, S., Kopparapu, K., Fisher, Z., Guo, R., Prakash, S., Srinivasan, P., Zaheer, M., Yu, F., and Kumar, S · 2023
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Tackling the abstraction and reasoning corpus (arc) with object-centric models and the mdl principle
Ferré, S · 2023
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Large language models are not strong abstract reasoners, 2023
Gendron, G., Bao, Q., Witbrock, M., and Dobbie, G · 2023
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Reinforced self-training (rest) for language modeling
Gulcehre, C., Paine, T. L., Srinivasan, S., Konyushkova, K., Weerts, L., Sharma, A., Siddhant, A., Ahern, A., Wang, M., Gu, C., et al · 2023
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Textbooks are all you need, 2023
Gunasekar, S., Zhang, Y., Aneja, J., Mendes, C. C. T., Giorno, A. D., Gopi, S., Javaheripi, M., Kauffmann, P., de Rosa, G., Saarikivi, O., Salim, A., Shah, S., Behl, H. S., Wang, X., Bubeck, S., Eldan, R., Kalai, A. T., Lee, Y. T., and Li, Y · 2023
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Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., and Mordatch, I · 2021
Cited alongside, same era.
Ferré, S · 2021
Cited alongside, same era.
Fast and flexible: Human program induction in abstract reasoning tasks
Johnson, A., Vong, W. K., Lake, B. M., and Gureckis, T. M · 2021
Cited alongside, same era.
Neural-guided, bidirectional program search for abstraction and reasoning
Alford, S., Gandhi, A., Rangamani, A., Banburski, A., Wang, T., Dandekar, S., Chin, J., Poggio, T., and Chin, P · 2022
Cited alongside, same era.
Program synthesis for integer sequence generation
Butt, N., Wiggers, A., Cohen, T., and Welling, M · 2022
Cited alongside, same era.
Discovering faster matrix multiplication algorithms with reinforcement learning
Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Mohammadamin, B., Novikov, A., Ruiz, F. J. R., Schrittwieser, J., Swirszcz, G., Silver, D., Hassabis, D., and Kohli, P · 2022
Cited alongside, same era.
Program synthesis for the oeis, 2022
Gauthier, T · 2022
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Domain-specific language for the abstraction and reasoning corpus, 2023
Hodel, M · 2023
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Faster sorting algorithms discovered using deep reinforcement learning
Mankowitz, D. J., Michi, A., Zhernov, A., Gelmi, M., Selvi, M., Paduraru, C., Leurent, E., Iqbal, S., Lespiau, J.-B., Ahern, A., et al · 2023
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Large language models as general pattern machines
Mirchandani, S., Xia, F., Florence, P., Ichter, B., Driess, D., Arenas, M. G., Rao, K., Sadigh, D., and Zeng, A · 2023
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Comparing humans, gpt-4, and gpt-4v on abstraction and reasoning tasks, 2023
Mitchell, M., Palmarini, A. B., and Moskvichev, A · 2023
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The conceptarc benchmark: Evaluating understanding and generalization in the arc domain, 2023
Moskvichev, A., Odouard, V. V., and Mitchell, M · 2023
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Learning math reasoning from self-sampled correct and partially-correct solutions, 2023
Ni, A., Inala, J. P., Wang, C., Polozov, O., Meek, C., Radev, D., and Gao, J · 2023
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Unraveling the arc puzzle: Mimicking human solutions with object-centric decision transformer
Park, J., Im, J., Hwang, S., Lim, M., Ualibekova, S., Kim, S., and Kim, S · 2023
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Beyond human data: Scaling self-training for problem-solving with language models, 2023
Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., Parisi, A., Kumar, A., Alemi, A., Rizkowsky, A., Nova, A., Adlam, B., Bohnet, B., Elsayed, G., Sedghi, H., Mordatch, I., Simpson, I., Gur, I., Snoek, J., Pennington, J., Hron, J., Kenealy, K., Swersky, K., Mahajan, K., Culp, L., Xiao, L., Bileschi, M. L., Constant, N., Novak, R., Liu, R., Warkentin, T., Qian, Y., Bansal, Y., Dyer, E., Neyshabur, B., Sohl-Dickstein, J., and Fiedel, N · 2023
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Xu, Y., Li, W., Vaezipoor, P., Sanner, S., and Khalil, E. B · 2023
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Language agent tree search unifies reasoning acting and planning in language models, 2023
Zhou, A., Yan, K., Shlapentokh-Rothman, M., Wang, H., and Wang, Y.-X · 2023
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https://lab42.global/arcathon/leaderboard/ , 2023
ARCathon Leaderboard · 2024
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https://www.kaggle.com/competitions/abstraction-and-reasoning-challenge/discussion/154597 , 2020
Icecuber · 2024
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https://www.kaggle.com/competitions/abstraction-and-reasoning-challenge/code , 2020
Kaggle Leaderboard · 2024
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