arXiv AI July 8, 2026 By Alexander Rombach, Chantale Lauer, Nijat Mehdiyev

Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design

arXiv:2607. 06175v1 Announce Type: cross Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output quality to the patterns present in the training data.

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

arXiv Machine Learning

Jul 7

Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

arXiv:2607. 02869v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathematical reasoning in language models.

By Anagha Radhakrishna Palandye, Rebecca Glick, Osheen Kaul

llms reinforcement-learning fine-tuning

arXiv AI

Jul 7

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.

By Saksham Sahai Srivastava, Vaneet Aggarwal

llms reinforcement-learning benchmarks safety

arXiv AI

Aug 3

AttriMem: Attribution-Guided Process Feedback for Agent Memory Construction

arXiv:2607. 21106v2 Announce Type: replace Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging.

By Qinfeng Li, Yuntai Bao, Xinyan Yu, Hongze Chen, Yanmin Liu, Wenqi Zhang, Xuhong Zhang

llms agents nlp reinforcement-learning benchmarks

arXiv AI

Jul 24

AttriMem: Attribution-Guided Process Feedback for Agent Memory Learning

arXiv:2607. 21106v1 Announce Type: new Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging.

By Qinfeng Li, Yuntai Bao, Xinyan Yu, Hongze Chen, Wenqi Zhang, Xuhong Zhang

llms agents nlp reinforcement-learning benchmarks

Hugging Face Trending Papers

Jun 2

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines. However, current reward evaluation relies on heterogeneous criteria such as rule-based verifiers, ground-truth references, procedural checklists, and complex rubrics, where a unified mechanism to integrate all types of evidence remains unexplored.

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 3

Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

arXiv:2606. 03980v1 Announce Type: new Abstract: Reward models (RMs) provide critical feedback signals for LLM post-training, notably in reinforced fine-tuning (RFT) and reinforcement learning (RL) pipelines.

By Tao Chen, Gangwei Jiang, Pengyu Cheng, Siyuan Huang, Yihao Liu, Jingwei Ni, Jiaqi Guo, Mengyu Zhou, Kai Tang, Junling Liu, Qinliang Su, Xiaoxi Jiang, Guanjun Jiang

llms agents reinforcement-learning fine-tuning benchmarks

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Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

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Skill-RM: Unifying Heterogeneous Evaluation Criteria via Agent Skill

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