Liang, Percy

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

Kim, M., Finn, C., & Liang, P. (2025) · Robotics: Science and Systems XXI

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 77

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candes, E., & Hashimoto, T. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 76

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

Dubois, Y., Li, C. X., Taori, R., Zhang, T., Gulrajani, I., Ba, J., Guestrin, C., Liang, P., & Hashimoto, T. (2023) · Advances in Neural Information Processing Systems 36

Reinforcement Learning in RoboticsSimulation Techniques and Applications

Cited by 76

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 74

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 66

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E. P., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 65

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) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 62

Evaluating Human-Language Model Interaction

Lee, M., Srivastava, M., Hardy, A., Thickstun, J., Durmus, E., Paranjape, A., Gerard-Ursin, I., Li, X. L., Ladhak, F., Rong, F., et al. (2022) · arXiv

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 61

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

Bommasani, R., Creel, K. A., Kumar, A., Jurafsky, D., & Liang, P. (2022) · arXiv

Ethics and Social Impacts of AIRecommender Systems and Techniques

Cited by 60

Naturalizing a Programming Language via Interactive Learning

Wang, S. I., Ginn, S., Liang, P., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 59

Holistic evaluation of large language models for medical tasks with MedHELM

Bedi, S., Cui, H., Fuentes, M., Unell, A., Wornow, M., Banda, J. M., Kotecha, N., Keyes, T., Mai, Y., Oez, M., et al. (2026) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 59

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

Khattab, O., Santhanam, K., Li, X. L., Hall, D., Liang, P., Potts, C., & Zaharia, M. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 52

LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

Park, J. S., Zou, C. Q., Kamphorst, J., Egan, N., Shaw, A., Hill, B. M., Cai, C., Morris, M. R., Liang, P., Willer, R., et al. (2024) · arXiv

Ethics and Social Impacts of AIComputational and Text Analysis Methods

Cited by 49

Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration

Liu, E. Z., Guu, K., Pasupat, P., Shi, T., & Liang, P. (2018) · arXiv

Reinforcement Learning in RoboticsWeb Data Mining and Analysis

Cited by 47

The Foundation Model Transparency Index

Bommasani, R., Klyman, K., Longpre, S., Kapoor, S., Maslej, N., Xiong, B., Zhang, D., & Liang, P. (2023) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 46

Considerations for governing open foundation models

Bommasani, R., Kapoor, S., Klyman, K., Longpre, S., Ramaswami, A., Zhang, D., Schaake, M., Ho, D. E., Narayanan, A., & Liang, P. (2024) · Science

Research, Science, and AcademiaLaw, AI, and Intellectual Property

Cited by 31

Robust distortion-free watermarks for language models

Kuditipudi, R., Thickstun, J., Hashimoto, T., & Liang, P. (2023) · arXiv

Advanced Steganography and Watermarking TechniquesAdversarial Robustness in Machine Learning

Cited by 23

The EOS Decision and Length Extrapolation

Newman, B., Hewitt, J., Liang, P., & Manning, C. D. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making

Li, M., Zhao, S., Wang, Q., Wang, K., Zhou, Y., Srivastava, S., Gokmen, C., Lee, T., Li, L. E., Zhang, R., et al. (2024) · arXiv

Robot Manipulation and LearningAI-based Problem Solving and Planning

Cited by 21

Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making

Li, M., Zhao, S., Wang, Q., Wang, K., Zhou, Y., Srivastava, S., Gokmen, C., Lee, T., Li, L., Zhang, R., et al. (2024) · Advances in Neural Information Processing Systems 37

Robot Manipulation and LearningAI-based Problem Solving and Planning

Cited by 21

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Dubois, Y., Galambosi, B., Liang, P., & Hashimoto, T. B. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 17

International AI Safety Report

Bengio, Y., Mindermann, S., Privitera, D., Besiroglu, T., Bommasani, R., Casper, S., Choi, Y., Fox, P., Garfinkel, B., Goldfarb, D., et al. (2025) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 16

Ecosystem Graphs: The Social Footprint of Foundation Models

Bommasani, R., Soylu, D., Liao, T., Creel, K., & Liang, P. (2023) · Research Square

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 14

s1: Simple test-time scaling

Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., & Hashimoto, T. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12