Segmenting Text and Learning Their Rewards for Improved RLHF in Language
  Model
Authors: name: Yueqin Yin, name: Shentao Yang, name: Yujia Xie, name: Ziyi Yang, name: Yuting Sun, name: Hany Awadalla, name: Weizhu Chen, name: Mingyuan Zhou

Reinforcement learning from human feedback (RLHF) has been widely adopted to
align language models (LMs) with human preference. Prior RLHF works typically
take a bandit formulation, which, though intuitive, ignores the sequential
nature of LM generation and can suffer from the sparse reward issue. While
recent works propose dense token-level RLHF, treating each token as an action
may be oversubtle to proper reward assignment. In this paper, we seek to get
the best of both by training and utilizing a segment-level reward model, which
assigns a reward to each semantically complete text segment that spans over a
short sequence of tokens. For reward learning, our method allows dynamic text
segmentation and compatibility with standard sequence-preference datasets. For
effective RL-based LM training against segment reward, we generalize the
classical scalar bandit reward normalizers into location-aware normalizer
functions and interpolate the segment reward for further densification. With
these designs, our method performs competitively on three popular RLHF
benchmarks for LM policy: AlpacaEval 2.0, Arena-Hard, and MT-Bench. Ablation
studies are conducted to further demonstrate our method.