11 citations · 52 across the 16 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Demystifying Reinforcement Learning Post-Training of Language Models
Donovan Clay, Saket Gollapudi, Sankar Harilal +4
Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling impressive reasoning, math, a…
cs.LG2024★ 5 cited
RewardBench: Evaluating Reward Models for Language Modeling
Nathan Lambert, Valentina Pyatkin, Jacob Morrison +9
Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluatio…