4 citations · 7 across the 7 of their papers we have counts for
3 papers · 1 filter
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
Kaiwen Wang, Rahul Kidambi, Ryan Sullivan +17
Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models tha…
Auctions with LLM Summaries
Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi +2
We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM), e.g., an ad auction in which the display…
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Gokul Swamy, Christoph Dann, Rahul Kidambi +2
We present Self-Play Preference Optimization (SPO), an algorithm for reinforcement learning from human feedback. Our approach is minimalist in that it does not require training a r…