4 papers
Mitigating Preference Hacking in Policy Optimization with Pessimism
Dhawal Gupta, Adam Fisch, Christoph Dann +1
This work tackles the problem of overoptimization in reinforcement learning from human feedback (RLHF), a prevalent technique for aligning models with human preferences. RLHF relie…
Design Considerations in Offline Preference-based RL
Alekh Agarwal, Christoph Dann, Teodor V. Marinov
Offline algorithms for Reinforcement Learning from Human Preferences (RLHF), which use only a fixed dataset of sampled responses given an input, and preference feedback among these…
Catoni Contextual Bandits are Robust to Heavy-tailed Rewards
Chenlu Ye, Yujia Jin, Alekh Agarwal +1
Typical contextual bandit algorithms assume that the rewards at each round lie in some fixed range , and their regret scales polynomially with this reward range . Howeve…
Preserving Expert-Level Privacy in Offline Reinforcement Learning
Navodita Sharma, Vishnu Vinod, Abhradeep Thakurta +4
The offline reinforcement learning (RL) problem aims to learn an optimal policy from historical data collected by one or more behavioural policies (experts) by interacting with an…