arXiv AI By Yexiang Liu, Wen Zhong, Sijie Zhu, Xin Gu, Fan Chen, Junxian Duan, Jie Cao, Longyin Wen, Zhenfang Chen

VlogReward: Learning Multi-Dimensional Evaluation for Vlog Editing

Read the original on arXiv AI →

arXiv:2607. 22632v1 Announce Type: new Abstract: The rapid rise of vlogs as a personalized storytelling medium has created a demand for automated systems to evaluate and refine vlog editing plans.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language
Sep 7

Generating Constructive Feedback on Stories via Reinforcement Learning

The paper introduces a reinforcement learning method to improve the quality of feedback generated by large language models for creative writers. By training with group relative policy optimization and a multi‑component reward that emphasizes tailored, actionable, and critical‑issue‑focused feedback, the authors demonstrate that their approach outperforms existing LLMs and baselines on three story corpora. The study shows that actionable suggestions are the key factor driving constructive feedback.

By Maja Stahl, Timon Ziegenbein, Henning Wachsmuth
arXiv AI
Jul 22

PlotTwist: A Creative Plot Generation Framework with Small Language Models

arXiv:2603. 16410v2 Announce Type: replace-cross Abstract: Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative that sustains global coherence, character development, pacing, tone consistency, and emotional progression.

By Abhinav Thorat, Ravi Kolla, Jyotin Goel, Madhav Kataria, Niranjan Pedanekar