11 citations · 21 across the 18 of their papers we have counts for
6 papers · 1 filter
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun +1
As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critic…
The Synergy of LLMs & RL Unlocks Offline Learning of Generalizable Language-Conditioned Policies with Low-fidelity Data
Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun +1
Developing autonomous agents capable of performing complex, multi-step decision-making tasks specified in natural language remains a significant challenge, particularly in realisti…
Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learning
Hao Sun, Mihaela van der Schaar
Aligning Large Language Models (LLMs) is crucial for enhancing their safety and utility. However, existing methods, primarily based on preference datasets, face challenges such as…
Supervised Fine-Tuning as Inverse Reinforcement Learning
Hao Sun
The prevailing approach to aligning Large Language Models (LLMs) typically relies on human or AI feedback and assumes access to specific types of preference datasets. In our work,…
Retrieval Augmented Thought Process for Private Data Handling in Healthcare
Thomas Pouplin, Hao Sun, Samuel Holt +1
Large Language Models (LLMs) have demonstrated the strong potential to assist both clinicians and the general public with their extensive medical knowledge. However, their applicat…
Dense Reward for Free in Reinforcement Learning from Human Feedback
Alex J. Chan, Hao Sun, Samuel Holt +1
Reinforcement Learning from Human Feedback (RLHF) has been credited as the key advance that has allowed Large Language Models (LLMs) to effectively follow instructions and produce…