---
title: "Who cites Volodymyr Mnih — 829 papers of our library"
description: "829 papers of the Elamur Research library cite the works of Volodymyr Mnih, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Volodymyr Mnih

1,031 citations of their works from 829 papers of our library (of the 149,439 whose bibliographies we read) · 110 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Volodymyr Mnih](https://md.research.elamur.ai/free/en/authors/volodymyr-mnih/)

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1. [**TensorFlow: A system for large-scale machine learning arXiv:1605.08695**Martin Abadi, Paul Barham, Jianmin Chen, et al.*2016 · 12,711 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-system-large-scale-machine/)
2. **[Effective Approaches to Attention-based Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/luong-2015-effective-approaches-attention-based-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 9,718 citations*
3. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arXiv:1603.04467**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,512 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)
4. [**Show, Attend and Tell: Neural Image Caption Generation with Visual Attention arXiv:1502.03044**Kelvin Xu, Jimmy Ba, Ryan Kiros, et al.*2015 · 7,670 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
5. [**Representation Learning with Contrastive Predictive Coding arXiv:1807.03748**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 7,022 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
6. [**Teaching Machines to Read and Comprehend arXiv:1506.03340**Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, et al.*2015 · 3,858 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/)
7. [**Towards A Rigorous Science of Interpretable Machine Learning arXiv:1702.08608**Finale Doshi-Velez, Been Kim*2017 · 3,226 citations*](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/)
8. **[Attention-based LSTM for Aspect-level Sentiment Classification](https://md.research.elamur.ai/free/en/papers/wang-2016-attention-based-lstm-aspect-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yequan Wang, Minlie Huang, Xiaoyan Zhu, et al.*2016 · 2,621 citations*
9. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
10. [**Attention-Based Models for Speech Recognition arXiv:1506.07503**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
11. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
12. [**The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables arXiv:1611.00712**Chris J. Maddison, Andriy Mnih, Yee Whye Teh*2016 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/maddison-2016-concrete-distribution-continuous-relaxation-discrete/)
13. [**Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer arXiv:1612.03928**Sergey Zagoruyko, Nikos Komodakis*2016 · 1,542 citations*](https://md.research.elamur.ai/free/en/papers/zagoruyko-2016-paying-more-attention-attention-improving/)
14. **[Deep Reinforcement Learning for Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2016-deep-reinforcement-learning-dialogue-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiwei Li, Will Monroe, Alan Ritter, et al.*2016 · 1,502 citations*
15. [**Sequence Level Training with Recurrent Neural Networks arXiv:1511.06732**Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, et al.*2015 · 1,370 citations*](https://md.research.elamur.ai/free/en/papers/ranzato-2015-sequence-level-training-recurrent-neural/)
16. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
17. **[ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs](https://md.research.elamur.ai/free/en/papers/yin-2016-abcnn-attention-based-convolutional-neural/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Wenpeng Yin, Hinrich Schütze, Bing Xiang, et al.*2016 · 1,055 citations*
18. [**BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain arXiv:1708.06733**Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg*2017 · 1,024 citations*](https://md.research.elamur.ai/free/en/papers/gu-2017-badnets-identifying-vulnerabilities-machine-learning/)
19. [**Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges arXiv:2103.11251 · Statistics Surveys, 2021**Cynthia Rudin; Chaofan Chen; Zhi Chen; Haiyang Huang; Lesia Semenova;, Chudi Zhong*2021 · 974 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2021-interpretable-machine-learning-fundamental/)
20. [**DRAW: A Recurrent Neural Network For Image Generation arXiv:1502.04623**Karol Gregor, Ivo Danihelka, Alex Graves, et al.*2015 · 947 citations*](https://md.research.elamur.ai/free/en/papers/gregor-2015-draw-recurrent-neural-network-image/)
21. **[Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yen-Chun Chen, Mohit Bansal*2018 · 826 citations*
22. [**Reasoning about Entailment with Neural Attention arXiv:1509.06664**Tim Rocktaschel, Edward Grefenstette, Karl Moritz Hermann, et al.*2015 · 802 citations*](https://md.research.elamur.ai/free/en/papers/rocktaschel-2015-reasoning-about-entailment-neural-attention/)
23. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 700 citations*
24. [**PathNet: Evolution Channels Gradient Descent in Super Neural Networks arXiv:1701.08734**Chrisantha Fernando, Dylan Banarse, Charles Blundell, et al.*2017 · 667 citations*](https://md.research.elamur.ai/free/en/papers/fernando-2017-pathnet-evolution-channels-gradient-descent/)
25. [**AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation arXiv:2308.08155**Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/)
26. [**RL²: Fast Reinforcement Learning via Slow Reinforcement Learning arXiv:1611.02779**Yan Duan, John Schulman, Xi Chen, et al.*2016 · 524 citations*](https://md.research.elamur.ai/free/en/papers/duan-2016-rl-2-fast-reinforcement-learning/)
27. [**Regularizing Neural Networks by Penalizing Confident Output Distributions arXiv:1701.06548**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 496 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
28. **[Semi-Supervised Sequence Modeling with Cross-View Training](https://md.research.elamur.ai/free/en/papers/clark-2018-semi-supervised-sequence-modeling-cross/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kevin Clark, Minh-Thang Luong, Christopher D. Manning, et al.*2018 · 494 citations*
29. **[On the Automatic Generation of Medical Imaging Reports](https://md.research.elamur.ai/free/en/papers/jing-2018-automatic-generation-medical-imaging-reports/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baoyu Jing, Pengtao Xie, Eric Xing*2018 · 484 citations*
30. **[An Unsupervised Neural Attention Model for Aspect Extraction](https://md.research.elamur.ai/free/en/papers/he-2017-unsupervised-neural-attention-model-aspect/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruidan He, Wee Sun Lee, Hwee Tou Ng, et al.*2017 · 477 citations*
31. [**Action-Conditional Video Prediction using Deep Networks in Atari Games arXiv:1507.08750**Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, et al.*2015 · 467 citations*](https://md.research.elamur.ai/free/en/papers/oh-2015-action-conditional-video-prediction-using/)
32. **[Gated-Attention Readers for Text Comprehension](https://md.research.elamur.ai/free/en/papers/dhingra-2016-gated-attention-readers-text-comprehension/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, et al.*2016 · 463 citations*
33. [**Massively Parallel Methods for Deep Reinforcement Learning arXiv:1507.04296**Arun Nair, Praveen Srinivasan, Sam Blackwell, et al.*2015 · 427 citations*](https://md.research.elamur.ai/free/en/papers/nair-2015-massively-parallel-methods-deep-reinforcement/)
34. **[WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://md.research.elamur.ai/free/en/papers/yao-2022-webshop-towards-scalable-real-world/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shunyu Yao, Howard Chen, John Yang, et al.*2022 · 420 citations*
35. [**Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding arXiv:1810.02338**Kexin Yi, Jiajun Wu, Chuang Gan, et al.*2018 · 407 citations*](https://md.research.elamur.ai/free/en/papers/yi-2018-neural-symbolic-vqa-disentangling-reasoning/)
36. [**Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551**Danijar Hafner, Timothy Lillicrap, Ian Fischer, et al.*2018 · 394 citations*](https://md.research.elamur.ai/free/en/papers/hafner-2018-learning-latent-dynamics-planning-pixels/)
37. **[Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents](https://md.research.elamur.ai/free/en/papers/machado-2017-revisiting-arcade-learning-environment-evaluation/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, et al.*2017 · 393 citations*
38. **[Tool Learning with Foundation Models](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 383 citations*
39. **[Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://md.research.elamur.ai/free/en/papers/tan-2019-learning-navigate-unseen-environments-back/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hao Tan, Licheng Yu, Mohit Bansal*2019 · 358 citations*
40. [**Progress & Compress: A scalable framework for continual learning arXiv:1805.06370**Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, et al.*2018 · 338 citations*](https://md.research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/)
41. [**Reinforcement Learning with Unsupervised Auxiliary Tasks arXiv:1611.05397**Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki, et al.*2016 · 319 citations*](https://md.research.elamur.ai/free/en/papers/jaderberg-2016-reinforcement-learning-unsupervised-auxiliary-tasks/)
42. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language arXiv:2204.00598**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 302 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
43. [**Exploration by Random Network Distillation arXiv:1810.12894**Yuri Burda, Harrison Edwards, Amos Storkey, et al.*2018 · 302 citations*](https://md.research.elamur.ai/free/en/papers/burda-2018-exploration-random-network-distillation/)
44. **[Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/qin-2018-robust-distant-supervision-relation-extraction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengda Qin, Weiran Xu, William Yang Wang*2018 · 298 citations*
45. **[xLSTM: Extended Long Short-Term Memory](https://md.research.elamur.ai/free/en/papers/beck-2024-xlstm-extended-long-short-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.*2024 · 296 citations*
46. [**Hierarchical Multiscale Recurrent Neural Networks arXiv:1609.01704**Junyoung Chung, Sungjin Ahn, Yoshua Bengio*2016 · 288 citations*](https://md.research.elamur.ai/free/en/papers/chung-2016-hierarchical-multiscale-recurrent-neural-networks/)
47. [**Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis ACM Computing Surveys 2019**Tal Ben-Nun, Torsten Hoefler*2018 · 275 citations*](https://md.research.elamur.ai/free/en/papers/nun-2018-demystifying-parallel-distributed-deep-learning/)
48. **[Mapping Instructions and Visual Observations to Actions with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/misra-2017-mapping-instructions-visual-observations-actions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dipendra Misra, John Langford, Yoav Artzi*2017 · 255 citations*
49. [**Transformers learn in-context by gradient descent arXiv:2212.07677**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 247 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
50. **[A Study of Reinforcement Learning for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/wu-2018-study-reinforcement-learning-neural-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lijun Wu, Fei Tian, Tao Qin, et al.*2018 · 234 citations*
51. [**Gradient Surgery for Multi-Task Learning arXiv:2001.06782**Tianhe Yu, Saurabh Kumar, Abhishek Gupta, et al.*2020 · 233 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-gradient-surgery-multi-task-learning/)
52. [**Scalable agent alignment via reward modeling: a research direction arXiv:1811.07871**Jan Leike, David Krueger, Tom Everitt, et al.*2018 · 227 citations*](https://md.research.elamur.ai/free/en/papers/leike-2018-scalable-agent-alignment-reward-modeling/)
53. [**Reinforced Self-Training (ReST) for Language Modeling arXiv:2308.08998**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
54. [**Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning arXiv:1910.00177**Xue Bin Peng, Aviral Kumar, Grace Zhang, et al.*2019 · 223 citations*](https://md.research.elamur.ai/free/en/papers/peng-2019-advantage-weighted-regression-simple-scalable/)
55. **[Unsupervised Question Decomposition for Question Answering](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Patrick Lewis, Wen-tau Yih, et al.*2020 · 222 citations*
56. **[Active Example Selection for In-Context Learning](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yiming Zhang, Shi Feng, Chenhao Tan*2022 · 209 citations*
57. [**A Convolutional Attention Network for Extreme Summarization of Source Code arXiv:1602.03001**Miltiadis Allamanis, Hao Peng, Charles Sutton*2016 · 198 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2016-convolutional-attention-network-extreme-summarization/)
58. **[A Dataset for Multi-Target Stance Detection](https://md.research.elamur.ai/free/en/papers/sobhani-2017-dataset-multi-target-stance-detection/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Parinaz Sobhani, Diana Inkpen, Xiaodan Zhu*2017 · 190 citations*
59. [**Grounded Language Learning in a Simulated 3D World arXiv:1706.06551**Karl Moritz Hermann, Felix Hill, Simon Green, et al.*2017 · 190 citations*](https://md.research.elamur.ai/free/en/papers/hermann-2017-grounded-language-learning-simulated-3d/)
60. [**Automatic Goal Generation for Reinforcement Learning Agents arXiv:1705.06366**Carlos Florensa, David Held, Xinyang Geng, et al.*2017 · 181 citations*](https://md.research.elamur.ai/free/en/papers/florensa-2017-automatic-goal-generation-reinforcement-learning/)
61. [**Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents arXiv:2302.01560**Zihao Wang, Shaofei Cai, Guanzhou Chen, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-describe-explain-plan-select-interactive/)
62. [**Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution arXiv:2309.16797**Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/fernando-2023-promptbreeder-self-referential-self-improvement/)
63. [**CLIPort: What and Where Pathways for Robotic Manipulation arXiv:2109.12098**Mohit Shridhar, Lucas Manuelli, Dieter Fox*2021 · 159 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2021-cliport-where-pathways-robotic-manipulation/)
64. **[Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-80-20-rule-high-entropy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shenzhi Wang, Le Yu, Chang Gao, et al.*2025 · 152 citations*
65. **[HyperTree Proof Search for Neural Theorem Proving](https://md.research.elamur.ai/free/en/papers/lample-2022-hypertree-proof-search-neural-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, et al.*2022 · 151 citations*
66. [**MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge arXiv:2206.08853**Linxi Fan, Guanzhi Wang, Yunfan Jiang, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/fan-2022-minedojo-building-open-ended-embodied/)
67. [**Efficient Processing of Deep Neural Networks: A Tutorial and Survey Proceedings of the IEEE 2017**Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.*2017 · 146 citations*](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/)
68. [**Diversity is All You Need: Learning Skills without a Reward Function arXiv:1802.06070**Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, et al.*2018 · 145 citations*](https://md.research.elamur.ai/free/en/papers/eysenbach-2018-diversity-all-need-learning-skills/)
69. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
70. [**Measuring the Intrinsic Dimension of Objective Landscapes arXiv:1804.08838**Chunyuan Li, Heerad Farkhoor, Rosanne Liu, et al.*2018 · 143 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-measuring-intrinsic-dimension-objective-landscapes/)
71. [**Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads arXiv:2008.09213**Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, et al.*2020 · 141 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/)
72. [**Reinforcement Learning Neural Turing Machines - Revised arXiv:1505.00521**Wojciech Zaremba, Ilya Sutskever*2015 · 139 citations*](https://md.research.elamur.ai/free/en/papers/zaremba-2015-reinforcement-learning-neural-turing-machines/)
73. [**Secrets of RLHF in Large Language Models Part I: PPO arXiv:2307.04964**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
74. [**DeepMind Lab arXiv:1612.03801**Charles Beattie, Joel Z. Leibo, Denis Teplyashin, et al.*2016 · 134 citations*](https://md.research.elamur.ai/free/en/papers/beattie-2016-deepmind-lab/)
75. [**Pre-Trained Language Models for Interactive Decision-Making arXiv:2202.01771**Shuang Li, Xavier Puig, Chris Paxton, et al.*2022 · 124 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-pre-trained-language-models-interactive/)
76. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
77. [**Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration arXiv:1802.08802**Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, et al.*2018 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-reinforcement-learning-web-interfaces-using/)
78. [**Gradient based sample selection for online continual learning arXiv:1903.08671**Rahaf Aljundi, Min Lin, Baptiste Goujaud, et al.*2019 · 115 citations*](https://md.research.elamur.ai/free/en/papers/aljundi-2019-gradient-based-sample-selection-online/)
79. [**Gymnasium: A Standard Interface for Reinforcement Learning Environments arXiv:2407.17032**Mark Towers, Ariel Kwiatkowski, Jordan Terry, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/towers-2024-gymnasium-standard-interface-reinforcement-learning/)
80. [**Linear Transformers Are Secretly Fast Weight Programmers arXiv:2102.11174**Imanol Schlag, Kazuki Irie, Jurgen Schmidhuber*2021 · 111 citations*](https://md.research.elamur.ai/free/en/papers/schlag-2021-linear-transformers-secretly-fast-weight/)
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