arXiv AI August 5, 2026 By Meicong Zhang, Tiancheng Su, Jiahao Cheng, Guoxiu He, Xinqi Tao, Dejia Song

Internalizing Academic Writing Workflows for Introduction Generation via Struct-Aware Policy Learning

arXiv:2608. 03138v1 Announce Type: cross Abstract: Generating a rigorous paper introduction with large language models (LLMs) remains challenging, since it requires coordinating background, gap identification, method and contribution within a coherent narrative.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 11

Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

arXiv:2604. 01029v2 Announce Type: replace-cross Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gains from genuine error correction.

By Jingjie Ning, Xueqi Li, Chengyu Yu

llms benchmarks

arXiv AI

Jul 28

Do Language Models Converge to Themselves? Recursive Self-Refinement as Textual Relaxation

arXiv:2607. 22653v1 Announce Type: new Abstract: Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model.

By Xuening Wu, Qianya Xu, Yanlan Kang, Zeping Chen, Yubin Liu, Shenqin Yin

llms

arXiv Machine Learning

Jul 21

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.

By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia

nlp fine-tuning benchmarks

arXiv AI

Jun 9

Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation

arXiv:2606. 08011v1 Announce Type: cross Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models.

By Boxuan Lyu, Haiyue Song, Zhi Qu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura

llms nlp reinforcement-learning

arXiv AI

Jun 3

Pretraining Language Models on Historical Text

arXiv:2606. 02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.

By Xiaoxi Luo, Zachary Shinnick, Niclas Griesshaber, Yixuan Wang, Junchi Yu, Freda Shi, Philip Torr, Yao Lu

llms fine-tuning benchmarks data-engineering

arXiv AI

Jun 12

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning

arXiv:2606. 13316v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a central technique for improving long-horizon reasoning in Large Language Models (LLMs).

By Xucong Wang, Ziyu Ma, Yong Wang, Shidong Yang, Hailang Huang, Renda Li, Pengkun Wang, Xiangxiang Chu

llms nlp reinforcement-learning

Related stories

Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

Do Language Models Converge to Themselves? Recursive Self-Refinement as Textual Relaxation

Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift

Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation

Pretraining Language Models on Historical Text

ReSum: Synergizing LLM Reasoning and Summarization with Reinforcement Learning