EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance Generation (2026 · 1 citation)
LLM judges remain weak at assessing stories. Prompting closed models is brittle, and fine-tuned open evaluators usually output scores without explicit reasoning. The authors propose EvolvR, a framework that teaches an open-source evaluator to reason over pairs of stories. The model works aspect by aspect, for example…
Triviality Corrected Endogenous Reward (2026)
Reinforcement fine-tuning for open-ended writing lacks verifiable rewards. It usually depends on costly proprietary LLM judges or on judge models trained with annotated preference data. The authors ask whether confidence-based endogenous rewards, which work for unsupervised RL in math, can serve writing instead. They…

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.

EvolvR: Self-Evolving Pairwise Reasoning for Story Evaluation to Enhance GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Xinda Wang, Zhengxu Hou, Yangshijie Zhang, et al.2026 · 1 citation
Triviality Corrected Endogenous RewardAnnual Meeting of the Association for Computational Linguistics (ACL)Xinda Wang, Zhengxu Hou, Yangshijie Zhang, et al.2026
Gradient-Guided Multi-Judge Prompt Optimization ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetChenZhuo Zhao et al.2026

Co-authors here

8 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated 1 November 2026