arXiv:2608. 09123v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) for open-ended tasks is challenging because responses must satisfy multidimensional criteria without following a single correct generation trajectory.
By Jinkun Hou, Zhuo Liu, Huimin Ren, Hongsheng Xin, Pan Zhou, Kun Zhan
The paper introduces Recursive Reward Improvement (RRI), a method that enhances recursive self‑improvement loops by using an adversary to expose and fix weaknesses in model‑generated rubrics for open‑ended tasks. In RRI, an adversary crafts a rubric‑gaming answer, an honest answer is generated without rubric exposure, and up to two repair steps adjust the rubric so the honest answer scores higher. Experiments on HealthBench Professional show that RRI boosts adjusted scores by up to 12.4 points on a 27B model and yields significant gains on difficult and writing tasks, while also improving performance on unseen benchmarks PRBench Hard and ProfBench.
By Wei Dai, Qianchu Liu, Guanghui Qin, Xing Han, Tristan Naumann, Paul Pu Liang, Sheng Zhang
The paper introduces a method for training large language models to generate diverse, high‑quality scientific ideas grounded in input documents. By using a set‑level reinforcement learning objective that rewards ideas for being distinct from earlier ones, the authors train an 8B model that outperforms Claude Opus 4.8 and GPT‑6 Astra on a contamination‑free benchmark. Human experts rate the model’s idea sets as equally promising while containing slightly more unique ideas, and automated experiments show comparable executability.
By Manya Wadhwa, Pranav Narayanan Venkit, Hiroaki Hayashi, Yu Li, Tuhin Chakrabarty, Chien-Sheng Wu
EvoRubric introduces a co‑evolutionary reinforcement learning framework that learns rubric‑driven evaluation criteria for open‑ended text generation. The system uses a shared policy that simultaneously generates responses and rubrics, while a frozen meta‑verifier and grader provide validity checks and scoring. Experiments on medical, writing, and science benchmarks show that EvoRubric outperforms static and other evolving‑rubric baselines, achieving higher average scores on five tasks.
By Xin Guan, Xiaomeng Hu, Shen Huang, Zhenyi Wang, Bo Zhang, Zijian Li, Pengjun Xie, Bo Liu, Jiuxin Cao
The paper investigates reflective prompt optimization (GEPA) for a multi‑agent deep‑research language‑model, focusing on a topic‑disjoint train/validation/test split to avoid memorization. Across three random seeds, the optimized prompts improve held‑out citation reward from 0.702 to 0.781, with a 95% confidence interval of [0.051, 0.107]. The study shows that the gain is driven by decomposed per‑dimension feedback rather than a single scalar reward, and that the improvement persists even with an out‑of‑family verifier and a hand‑written version of the discovered recipe.
By Elias Lumer, Sahil Sen, Vamse Kumar Subbiah, Kevin Paul
arXiv:2607. 28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery.
By Vaibhava Lakshmi Ravideshik, Mayank Kejriwal