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…
FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
Anikait Singh, Sheryl Hsu, Kyle Hsu +5
Effective personalization of LLMs is critical for a broad range of user-interfacing applications such as virtual assistants and content curation. Inspired by the strong in-context…
GIANTS: Generative Insight Anticipation from Scientific Literature
Joy He-Yueya, Anikait Singh, Ge Gao +5
Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific discovery, their ability to per…
Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone
Max Sobol Mark, Tian Gao, Georgia Gabriela Sampaio +4
Recent advances in learning decision-making policies can largely be attributed to training expressive policy models, largely via imitation learning. While imitation learning discar…