LongReward: Improving Long-context Large Language Models with AI Feedback
LongReward improves long-context LLMs by applying AI feedback to long-text instruction data via a multi-granularity reward model that evaluates both global coherence and local accuracy.
Published 20254 citationsPaper ↗
70%
OverallHighly rated
?
OverallHighly ratedVote to see the scoreThe exact score shows once you've voted, so every vote is your own call. The first half of each home page shelf shows its scores.
Readers
–
Only vote on papers you've read. Sign in with GitHub to vote.
AI panel4/20reviewers recommend it
lenient 2/5
medium 2/10
strict 0/5
AI panel?Vote to see what the 20 AI reviewers said
LongReward offers a timely AI-feedback framework for long-context reward tuning, but its narrow evaluation, unclear baselines, and unproven scalability leave it an incremental, unconvincing optimization with no verified long-context gains.
Abstract
Jiajie Zhang, Zhongni Hou, Xin Lv, Shulin Cao, Zhenyu Hou, Yilin Niu, Lei Hou, Yuxiao Dong, Ling Feng, Juanzi Li. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.