20 citations · 20 across the 2 of their papers we have counts for
2 papers
cs.CL2024
RRM: Robust Reward Model Training Mitigates Reward Hacking
Tianqi Liu, Wei Xiong, Jie Ren +15
Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to sp…
cs.LG2024★ 20 cited
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu +1
Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natura…