Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026 · 8 citations)
Generative reward models (GRMs) judge LLM outputs using chain-of-thought (CoT) reasoning. They have two weaknesses. They spend reasoning effort on every input, including easy ones. They also pick among sampled reasoning chains by voting, which cannot separate sound chains from ones that only look convincing. The…
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026 · 6 citations)
Supervised fine-tuning (SFT) is usually treated as a reliable way to specialize LLMs. The authors show, however, that converged models still get part of their own training set wrong. They call this the Incomplete Learning Phenomenon (ILP), distinguish it from catastrophic forgetting and machine unlearning, and…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal UncertaintyFindings of the Association for Computational LinguisticsChao Xue, Yao Wang, Mengqiao Liu, et al.2026 · 8 citations
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Chao Xue, Yao Wang, Mengqiao Liu, et al.2026 · 6 citations
C-World: A Computer Use Agent Environment Creator ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZiqiao Xi et al.2026

Co-authors here

11 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 14 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated 30 October 2026