3.4k citations · 3.5k across the 13 of their papers we have counts for
6 papers · 1 filter
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
Khimya Khetarpal, Zhaohan Daniel Guo, Bernardo Avila Pires +7
Learning a good representation is a crucial challenge for Reinforcement Learning (RL) agents. Self-predictive learning provides means to jointly learn a latent representation and d…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Understanding the performance gap between online and offline alignment algorithms
Yunhao Tang, Daniel Zhaohan Guo, Zeyu Zheng +8
Reinforcement learning from human feedback (RLHF) is the canonical framework for large language model alignment. However, rising popularity in offline alignment algorithms challeng…
Human Alignment of Large Language Models through Online Preference Optimisation
Daniele Calandriello, Daniel Guo, Remi Munos +10
Ensuring alignment of language models' outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensiv…
Off-policy Distributional Q(): Distributional RL without Importance Sampling
Yunhao Tang, Mark Rowland, Rémi Munos +2
We introduce off-policy distributional Q(), a new addition to the family of off-policy distributional evaluation algorithms. Off-policy distributional Q() does not apply impo…
Generalized Preference Optimization: A Unified Approach to Offline Alignment
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng +7
Offline preference optimization allows fine-tuning large models directly from offline data, and has proved effective in recent alignment practices. We propose generalized preferenc…