Aggarwal, Pranjal

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & da Silva, B. C. (2024) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 92

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & Castro da Silva, B. (2026) · ACM Computing Surveys

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 92

GEO: Generative Engine Optimization

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Information Retrieval and Search BehaviorWeb visibility and informetrics

Cited by 62

AutoMix: Automatically Mixing Language Models

Aggarwal, P., Madaan, A., Anand, A., Potharaju, S. P., Mishra, S., Zhou, P., Gupta, A., Rajagopal, D., Kappaganthu, K., Yang, Y., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 19

AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement

Aggarwal, P., Parno, B., & Welleck, S. (2024) · arXiv

Logic, programming, and type systemsSoftware Engineering Research

Cited by 4

L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning

Aggarwal, P., & Welleck, S. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2

On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists

Kim, S., Yoon, D., Gashteovski, K., Suk, J., Baek, J., Aggarwal, P., Wu, I., Zaverkin, V., Petkoski, S., Schrider, D. R., et al. (2026) · arXiv

Academic Writing and PublishingAcademic Publishing and Open Access

Cited by 1