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Recent advances in language modeling and vision stem from training large models on diverse, multi-task data.
A value for n-person games
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Dynamic Programming
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Actor-critic algorithms
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Matplotlib: A 2d graphics environment
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The arcade learning environment: An evaluation platform for general agents
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Markov decision processes: discrete stochastic dynamic programming
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Deterministic policy gradient algorithms
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
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Actor-mimic: Deep multitask and transfer reinforcement learning
Parisotto, E., Ba, J. L., and Salakhutdinov, R · 2015
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Rusu, A. A., Colmenarejo, S. G., Gulcehre, C., Desjardins, G., Kirkpatrick, J., Pascanu, R., Mnih, V., Kavukcuoglu, K., and Hadsell, R · 2015
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End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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Pixel recurrent neural networks
Van Den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 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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Successor features for transfer in reinforcement learning
Barreto, A., Dabney, W., Munos, R., Hunt, J. J., Schaul, T., van Hasselt, H. P., and Silver, D · 2017
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A distributional perspective on reinforcement learning
Bellemare, M. G., Dabney, W., and Munos, R · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., and Amodei, D · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Mastering the game of go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al · 2017
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Distral: Robust multitask reinforcement learning
Teh, Y., Bapst, V., Czarnecki, W. M., Quan, J., Kirkpatrick, J., Hadsell, R., Heess, N., and Pascanu, R · 2017
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Jax: composable transformations of python+ numpy programs
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., et al · 2018
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., Hoof, H., and Meger, D · 2018
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Divide-and-conquer reinforcement learning
Ghosh, D., Singh, A., Rajeswaran, A., Kumar, V., and Levine, S · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Learning an embedding space for transferable robot skills
Hausman, K., Springenberg, J. T., Wang, Z., Heess, N., and Riedmiller, M · 2018
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An empirical model of large-batch training
McCandlish, S., Kaplan, J., Amodei, D., and Team, O. D · 2018
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Multi-task learning as multi-objective optimization
Sener, O. and Koltun, V · 2018
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Tassa, Y., Doron, Y., Muldal, A., Erez, T., Li, Y., Casas, D. d. L., Budden, D., Abdolmaleki, A., Merel, J., Lefrancq, A., et al · 2018
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Deep reinforcement learning and the deadly triad
Van Hasselt, H., Doron, Y., Strub, F., Hessel, M., Sonnerat, N., and Modayil, J · 2018
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Better exploration with optimistic actor critic
Ciosek, K., Vuong, Q., Loftin, R., and Hofmann, K · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Multi-task deep reinforcement learning with popart
Hessel, M., Soyer, H., Espeholt, L., Czarnecki, W., Schmitt, S., and van Hasselt, H · 2019
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Model based reinforcement learning for atari
Kaiser, Ł., Babaeizadeh, M., Miłos, P., Osiński, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al · 2019
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Efficient off-policy meta-reinforcement learning via probabilistic context variables
Rakelly, K., Zhou, A., Finn, C., Levine, S., and Quillen, D · 2019
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When to use parametric models in reinforcement learning?
Van Hasselt, H. P., Hessel, M., and Aslanides, J · 2019
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Learning dexterous in-hand manipulation
Andrychowicz, O. M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Plappert, M., Powell, G., Ray, A., et al · 2020
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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
Provable benefits of representational transfer in reinforcement learning
Agarwal, A., Song, Y., Sun, W., Wang, K., Wang, M., and Zhang, X · 2023
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Efficient online reinforcement learning with offline data
Ball, P. J., Smith, L., Kostrikov, I., and Levine, S · 2023
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A. M., Pillai, T. S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., and Fiedel, N · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al · 2023
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Cited alongside, same era.
Just pick a sign: Optimizing deep multitask models with gradient sign dropout
Chen, Z., Ngiam, J., Huang, Y., Luong, T., Kretzschmar, H., Chai, Y., and Anguelov, D · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
Array programming with numpy
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al · 2020
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Scaling laws for autoregressive generative modeling
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Image augmentation is all you need: Regularizing deep reinforcement learning from pixels
Kostrikov, I., Yarats, D., and Fergus, R · 2020
Cited alongside, same era.
For sale: State-action representation learning for deep reinforcement learning
Fujimoto, S., Chang, W.-D., Smith, E., Gu, S. S., Precup, D., and Meger, D · 2023
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Mastering diverse domains through world models
Hafner, D., Pasukonis, J., Ba, J., and Lillicrap, T · 2023
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Td-mpc2: Scalable, robust world models for continuous control
Hansen, N., Su, H., and Wang, X · 2023
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Champion-level drone racing using deep reinforcement learning
Kaufmann, E., Bauersfeld, L., Loquercio, A., Müller, M., Koltun, V., and Scaramuzza, D · 2023
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Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al · 2023
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Offline q-learning on diverse multi-task data both scales and generalizes
Kumar, A., Agarwal, R., Geng, X., Tucker, G., and Levine, S · 2023
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On the theory of risk-aware agents: Bridging actor-critic and economics
Nauman, M. and Cygan, M · 2023
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Bigger, better, faster: Human-level atari with human-level efficiency
Schwarzer, M., Ceron, J. S. O., Courville, A., Bellemare, M. G., Agarwal, R., and Castro, P. S · 2023
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Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Unidexgrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning
Wan, W., Geng, H., Liu, Y., Shan, Z., Yang, Y., Yi, L., and Wang, H · 2023
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Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy
Xu, Y., Wan, W., Zhang, J., Liu, H., Shan, Z., Shen, H., Wang, R., Geng, H., Weng, Y., Chen, J., et al · 2023
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Zitkovich, B., Yu, T., Xu, S., Xu, P., Xiao, T., Xia, F., Wu, J., Wohlhart, P., Welker, S., Wahid, A., et al · 2023
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Efficient multi-task reinforcement learning with cross-task policy guidance
He, J., Li, K., Zang, Y., Fu, H., Fu, Q., Xing, J., and Cheng, J · 2024
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Openvla: An open-source vision-language-action model
Kim, M. J., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Lam, G., Sanketi, P., et al · 2024
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Simba: Simplicity bias for scaling up parameters in deep reinforcement learning
Lee, H., Hwang, D., Kim, D., Kim, H., Tai, J. J., Subramanian, K., Wurman, P. R., Choo, J., Stone, P., and Seno, T · 2024
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Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., et al · 2024
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Moka: Open-vocabulary robotic manipulation through mark-based visual prompting
Liu, F., Fang, K., Abbeel, P., and Levine, S · 2024
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Scaling laws for fine-grained mixture of experts
Ludziejewski, J., Krajewski, J., Adamczewski, K., Pióro, M., Krutul, M., Antoniak, S., Ciebiera, K., Król, K., Odrzygóźdź, T., Sankowski, P., et al · 2024
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Policy agnostic rl: Offline rl and online rl fine-tuning of any class and backbone
Mark, M. S., Gao, T., Sampaio, G. G., Srirama, M. K., Sharma, A., Finn, C., and Kumar, A · 2024
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Dinov2: Learning robust visual features without supervision
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al · 2024
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Is value learning really the main bottleneck in offline rl?
Park, S., Frans, K., Levine, S., and Kumar, A · 2024
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Humanoidbench: Simulated humanoid benchmark for whole-body locomotion and manipulation
Sferrazza, C., Huang, D.-M., Lin, X., Lee, Y., and Abbeel, P · 2024
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Offline actor-critic reinforcement learning scales to large models
Springenberg, J. T., Abdolmaleki, A., Zhang, J., Groth, O., Bloesch, M., Lampe, T., Brakel, P., Bechtle, S. M. E., Kapturowski, S., Hafner, R., et al · 2024
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Octo: An open-source generalist robot policy
Team, O. M., Ghosh, D., Walke, H., Pertsch, K., Black, K., Mees, O., Dasari, S., Hejna, J., Kreiman, T., Xu, C., et al · 2024
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Latent action pretraining from videos
Ye, S., Jang, J., Jeon, B., Joo, S., Yang, J., Peng, B., Mandlekar, A., Tan, R., Chao, Y.-W., Lin, B. Y., et al · 2024
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Efficient online reinforcement learning fine-tuning need not retain offline data
Zhou, Z., Peng, A., Li, Q., Levine, S., and Kumar, A · 2024
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Otter: A vision-language-action model with text-aware visual feature extraction
Huang, H., Liu, F., Fu, L., Wu, T., Mukadam, M., Malik, J., Goldberg, K., and Abbeel, P · 2025
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Hyperspherical normalization for scalable deep reinforcement learning
Lee, H., Lee, Y., Seno, T., Kim, D., Stone, P., and Choo, J · 2025
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Park, S., Li, Q., and Levine, S · 2025
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Value-based deep rl scales predictably
Rybkin, O., Nauman, M., Fu, P., Snell, C., Abbeel, P., Levine, S., and Kumar, A · 2025
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