Pulkit Agrawal

OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning

2020 · arXiv (Cornell University)

Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited. However, in many practical applications, the …

Value Augmented Sampling for Language Model Alignment and Personalization

2024 · ArXiv.org

Aligning Large Language Models (LLMs) to cater to different human preferences, learning new skills, and unlearning harmful behavior is an important problem. Search-based methods, such as Best-of-N or Monte-Carlo Tree Search, are performant, but impractical …