arXiv AI June 4, 2026 By Tianyang Zhou, Wenbo Chen, Pierre Jinghong Liang, Leman Akoglu

Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text

arXiv:2605. 29076v2 Announce Type: replace-cross Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while discrete prompt optimization offers human-readable instructions but struggles with performance and scalability.

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

arXiv AI

Jun 10

Learning Evidence Highlighting for Frozen LLMs

arXiv:2604. 22565v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts.

By Shaoang Li, Yanhang Shi, Yufei Li, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Frank Shyu, Luke Simon, Sandeep Pandey, Xi Liu, Jian Li

llms nlp reinforcement-learning

arXiv AI

Aug 3

TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter

arXiv:2607. 28657v1 Announce Type: new Abstract: Large Language Models (LLMs) often require carefully crafted prompts to unlock their full potential, which can be a barrier for non-expert users.

By Oliver Savolainen, Emanuele Bastianelli, Hosein Azarbonyad

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 5

Learning to Translate from Soft to Hard LLM Prompts

arXiv:2605. 27642v2 Announce Type: replace-cross Abstract: Soft prompting, also known as continuous prompting, is a parameter-efficient method for tuning LLMs to specific tasks.

By Pitipat Kongsomjit, Suryansh Goyal, Jacob Whitehill

llms rag safety

arXiv AI

Jul 21

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches

arXiv:2607. 16205v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has emerged as a standard approach for enhancing reasoning in large language models, which typically optimizes the policy by contrasting multiple self generated rollouts.

By Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Liwei Qian, Xin Pei, Jizhou Huang

llms reinforcement-learning benchmarks

arXiv AI

Jun 15

Implicit Reasoning for Large Language Model-based Generative Recommendation

arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.

By Yinhan He, Liam Collins, Bhuvesh Kumar, Jundong Li, Neil Shah, Donald Loveland

llms rag safety

arXiv Machine Learning

Jun 17

Learning to Refine Hidden States for Reliable LLM Reasoning

arXiv:2606. 17524v1 Announce Type: new Abstract: Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where early hidden-state errors may propagate to incorrect predictions.

By Chia-Hsuan Hsu, Jui-Ming Yao

llms reinforcement-learning benchmarks

Related stories

Learning Evidence Highlighting for Frozen LLMs

TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter

Learning to Translate from Soft to Hard LLM Prompts

It Takes 8 Tokens: Weak-to-Strong Off-Policy RL via Auxiliary Branches

Implicit Reasoning for Large Language Model-based Generative Recommendation

Learning to Refine Hidden States for Reliable LLM Reasoning