Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems.
AMARIS: A Memory-Augmented Rubric Improvement System for Rubric-Based Reinforcement Learning
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Adobe Inc.（Adobe公司）；Department of Computer Science, University of California, Santa Barbara（加州大学圣芭芭拉分校计算机科学系）
Towards Faithful and Controllable Personalization via Critique-Post-Edit Reinforcement Learning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Electronic Science and Technology of China（电子科技大学）；South China Agricultural University（华南农业大学）
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Tongyi Lab , Alibaba Group（通义实验室，阿里巴巴集团）；Alibaba Group（阿里巴巴集团）；SUAT（深圳大学）
Reinforcement Learning for Micro-Level Claims Reserving
Centre for Actuarial Studies, Department of Economics, The University of Melbourne（澳大利亚墨尔本大学经济系精算研究所以及精算研究中心）；University of the Witwatersrand（沃斯兰德大学）；School of Risk and Actuarial Studies, UNSW Australia Business School, UNSW Sydney（新南威尔士大学悉尼分校商学院风险与精算研究所以及精算研究学院）；Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）
GIFT: LLM-Guided State-Reward Interface for Financial Reinforcement Learning
East China University of Science and Technology（华东理工大学）；University of Science and Technology of China（中国科学技术大学）；Southwestern University of Finance and Economics（西南财经大学）；University of Sydney（悉尼大学）；City University of Hong Kong（香港城市大学）；Northeastern University（东北大学）；The Ohio State University（俄亥俄州立大学）；National University of Singapore（新加坡国立大学）
ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
Shanghai Jiao Tong University, School of Mathematical Sciences（上海交通大学数学科学学院）；DP Technology（DP技术）；University of Science and Technology of China（中国科学技术大学）；AI for Science Institute（AI for Science研究院）
An AI agent for treatment reasoning over a biomedical tool universe
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute（哈佛医学院伊万和弗朗西斯卡·伯科维茨家族生活实验室合作与克莱利研究所）；Cardiovascular Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院心脏病学部，布里格姆和妇女医院）；The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院人工智能与人类健康系）；The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai and Mount Sinai Health System（西奈山伊坎医学院和西奈山医疗系统数字健康研究所）；Mindich Child Health and Development Institute and the Departments of Pediatrics and Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院Mindich儿童健康与发展研究所及儿科学和遗传学与基因组科学系）；Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院Fuster心脏医院）；Mount Sinai AI Assurance Lab, Mount Sinai Health System（西奈山医疗系统AI保证实验室）；Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院重症监护医学研究所）；Department of Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院医学系）；ATHENA-R1 Evaluation Group（ATHENA-R1评估组）
