PhD student, Georgia Institute of Technology
2 papers at NeurIPS 2025
We propose an uncertainty-based routing framework that efficiently complements a fast RM with a strong but costly LLM judge.
We propose Think-RM, a training framework for generative reward models that enables long-horizon reasoning, and introduce a pairwise RLHF pipeline that directly optimizes policies using pairwise preference rewards.
