19 citations · 31 across the 5 of their papers we have counts for
5 papers
ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning
Hosein Hasanbeig, Hiteshi Sharma, Leo Betthauser +2
From grading papers to summarizing medical documents, large language models (LLMs) are evermore used for evaluation of text generated by humans and AI alike. However, despite their…
Fine-Tuning Language Models with Advantage-Induced Policy Alignment
Banghua Zhu, Hiteshi Sharma, Felipe Vieira Frujeri +4
Reinforcement learning from human feedback (RLHF) has emerged as a reliable approach to aligning large language models (LLMs) to human preferences. Among the plethora of RLHF techn…
Randomized Policy Learning for Continuous State and Action MDPs
Hiteshi Sharma, Rahul Jain
Deep reinforcement learning methods have achieved state-of-the-art results in a variety of challenging, high-dimensional domains ranging from video games to locomotion. The key to…
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision Processes
Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo +2
Model-free reinforcement learning is known to be memory and computation efficient and more amendable to large scale problems. In this paper, two model-free algorithms are introduce…
An Empirical Dynamic Programming Algorithm for Continuous MDPs
William B. Haskell, Rahul Jain, Hiteshi Sharma +1
We propose universal randomized function approximation-based empirical value iteration (EVI) algorithms for Markov decision processes. The `empirical' nature comes from each iterat…