4 papers
Test-Time Alignment via Hypothesis Reweighting
Yoonho Lee, Jonathan Williams, Henrik Marklund +4
Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning or long-context conditioning are…
Out-of-Distribution Detection Methods Answer the Wrong Questions
Yucen Lily Li, Daohan Lu, Polina Kirichenko +4
To detect distribution shifts and improve model safety, many out-of-distribution (OOD) detection methods rely on the predictive uncertainty or features of supervised models trained…
Continual Learning as Computationally Constrained Reinforcement Learning
Saurabh Kumar, Henrik Marklund, Ashish Rao +4
An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities…
Maintaining Plasticity in Continual Learning via Regenerative Regularization
Saurabh Kumar, Henrik Marklund, Benjamin Van Roy
In continual learning, plasticity refers to the ability of an agent to quickly adapt to new information. Neural networks are known to lose plasticity when processing non-stationary…