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Quality-Diversity (QD) approaches are a promising direction to develop open-ended processes as they can discover archives of high-quality solutions across diverse niches.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · 1901
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Clune, J. (2019) · 1905
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Bop-elites, a bayesian optimisation algorithm for quality-diversity search
Kent, P. and Branke, J. (2020) · 2005
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Real-Parameter Black-Box Optimization Benchmarking 2010: Experimental Setup
Hansen, N., Auger, A., Finck, S., and Ros, R. (2010) · 2010
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Novelty search and the problem with objectives
Lehman, J. and Stanley, K. O. (2011) · 2011
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Behavioral repertoire learning in robotics
Cully, A. and Mouret, J.-B. (2013) · 2013
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Robots that can adapt like animals
Cully, A., Clune, J., Tarapore, D., and Mouret, J.-B. (2015) · 2015
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Illuminating search spaces by mapping elites
Mouret, J.-B. and Clune, J. (2015) · 2015
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Quality diversity: A new frontier for evolutionary computation
Pugh, J. K., Soros, L. B., and Stanley, K. O. (2016) · 2016
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Quality and diversity optimization: A unifying modular framework
Cully, A. and Demiris, Y. (2017) · 2017
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Open-endedness: The last grand challenge you’ve never heard of
Stanley, K. O. (2017) · 2017
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Reset-free trial-and-error learning for robot damage recovery
Chatzilygeroudis, K., Vassiliades, V., and Mouret, J.-B. (2018) · 2018
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Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents
Conti, E., Madhavan, V., Petroski Such, F., Lehman, J., Stanley, K., and Clune, J. (2018) · 2018
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Data-efficient design exploration through surrogate-assisted illumination
Gaier, A., Asteroth, A., and Mouret, J.-B. (2018) · 2018
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Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Vassiliades, V. and Mouret, J.-B. (2018) · 2018
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Are quality diversity algorithms better at generating stepping stones than objective-based search?
Gaier, A., Asteroth, A., and Mouret, J.-B. (2019) · 2019
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Procedural content generation through quality diversity
Gravina, D., Khalifa, A., Liapis, A., Togelius, J., and Yannakakis, G. N. (2019) · 2019
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Why Open-Endedness Matters
Stanley, K. O. (2019) · 2019
Cited alongside, same era.
Scaling map-elites to deep neuroevolution
Colas, C., Madhavan, V., Huizinga, J., and Clune, J. (2020) · 2020
Cited alongside, same era.
Covariance matrix adaptation for the rapid illumination of behavior space
Fontaine, M. C., Togelius, J., Nikolaidis, S., and Hoover, A. K. (2020) · 2020
Cited alongside, same era.
Discovering representations for black-box optimization
Gaier, A., Asteroth, A., and Mouret, J.-B. (2020) · 2020
Cited alongside, same era.
Model-based quality-diversity search for efficient robot learning
Keller, L., Tanneberg, D., Stark, S., and Peters, J. (2020) · 2020
Cited alongside, same era.
Quality-diversity optimization: a novel branch of stochastic optimization
Chatzilygeroudis, K., Cully, A., Vassiliades, V., and Mouret, J.-B. (2021) · 2021
Cited alongside, same era.
Flageat Lim, Manon, B. and Cully, A. (2023) · 2023
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Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al. (2023) · 2023
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Evolution through large models
Lehman, J., Gordon, J., Jain, S., Ndousse, K., Yeh, C., and Stanley, K. O. (2023) · 2023
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Large language models as evolutionary optimizers
Liu, S., Chen, C., Qu, X., Tang, K., and Ong, Y.-S. (2023) · 2023
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Language model crossover: Variation through few-shot prompting
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Multi-emitter map-elites: improving quality, diversity and data efficiency with heterogeneous sets of emitters
Cully, A. (2021) · 2021
Cited alongside, same era.
Differentiable quality diversity
Fontaine, M. and Nikolaidis, S. (2021) · 2021
Cited alongside, same era.
Just ask for generalization
Jang, E. (2021) · 2021
Cited alongside, same era.
Policy gradient assisted map-elites
Nilsson, O. and Cully, A. (2021) · 2021
Cited alongside, same era.
In-context reinforcement learning with algorithm distillation
Laskin, M., Wang, L., Oh, J., Parisotto, E., Spencer, S., Steigerwald, R., Strouse, D., Hansen, S. S., Filos, A., Brooks, E., et al. (2022) · 2022
Cited alongside, same era.
Dynamics-aware quality-diversity for efficient learning of skill repertoires
Lim, B., Grillotti, L., Bernasconi, L., and Cully, A. (2022) · 2022
Cited alongside, same era.
Meyerson, E., Nelson, M. J., Bradley, H., Moradi, A., Hoover, A. K., and Lehman, J. (2023) · 2023
Later among the works it cites.
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) · 2023
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X. (2023) · 2023
Later among the works it cites.
Using large language models for hyperparameter optimization
Zhang, M. R., Desai, N., Bae, J., Lorraine, J., and Ba, J. (2023) · 2023
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Evoprompting: Language models for code-level neural architecture search
Chen, A., Dohan, D., and So, D. (2024) · 2024
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Large language models as evolution strategies
Lange, R. T., Tian, Y., and Tang, Y. (2024) · 2024
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Integer tokenization is insane
Millidge, B. (2024) · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T., Alayrac, J.-b., Soricut, R., Lazaridou, A., Firat, O., Schrittwieser, J., et al. (2024) · 2024
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Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · 2024
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Rainbow teaming: Open-ended generation of diverse adversarial prompts
Samvelyan, M., Raparthy, S. C., Lupu, A., Hambro, E., Markosyan, A. H., Bhatt, M., Mao, Y., Jiang, M., Parker-Holder, J., Foerster, J., et al. (2024) · 2024
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Tokenization counts: the impact of tokenization on arithmetic in frontier llms
Singh, A. K. and Strouse, D. (2024) · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T. (2024) · 2024
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