14 citations · 54 across the 21 of their papers we have counts for
4 papers · 1 filter
Learning in complex action spaces without policy gradients
Arash Tavakoli, Sina Ghiassian, Nemanja Rakićević
While conventional wisdom holds that policy gradient methods are better suited to complex action spaces than action-value methods, foundational work has shown that the two paradigm…
Soft Preference Optimization: Aligning Language Models to Expert Distributions
Arsalan Sharifnassab, Saber Salehkaleybar, Sina Ghiassian +2
We propose Soft Preference Optimization (SPO), a method for aligning generative models, such as Large Language Models (LLMs), with human preferences, without the need for a reward…
On the Importance of Uncertainty in Decision-Making with Large Language Models
Nicolò Felicioni, Lucas Maystre, Sina Ghiassian +1
We investigate the role of uncertainty in decision-making problems with natural language as input. For such tasks, using Large Language Models as agents has become the norm. Howeve…
In-context Exploration-Exploitation for Reinforcement Learning
Zhenwen Dai, Federico Tomasi, Sina Ghiassian
In-context learning is a promising approach for online policy learning of offline reinforcement learning (RL) methods, which can be achieved at inference time without gradient opti…