arXiv AI July 24, 2026 By Zihang Tian, Jingsen Zhang, Rui Li, Xiaohe Bo, Yuanzi Li, Xu Chen

ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents

arXiv:2606. 21262v2 Announce Type: replace Abstract: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad.

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

arXiv AI

Jun 3

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

arXiv:2606. 03108v1 Announce Type: new Abstract: Autonomous LLM training is often framed as recipe search, which leaves the training harness largely static.

By Guhong Chen, Yingcheng Shi, Yongbin Li, Binhua Li, Xander Xu, Hu Wei, Shiwen Ni, Min Yang, Jieping Ye

llms agents reinforcement-learning

arXiv AI

Jun 2

COMAP: Co-Evolving World Models and Agent Policies for LLM Agents

arXiv:2606. 02372v1 Announce Type: new Abstract: Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution.

By Youwei Liu, Jian Wang, Hanlin Wang, Wenjie Li

llms agents robotics efficiency benchmarks

arXiv AI

1d ago

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.

By Huan Zhang, Mingju Chen, Dongxu Zhou, Can Lv, Heng Chang, Sen Cui, Faguo Wu, Shiji Zhou

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 4

RUBAS: Rubric-Based Reinforcement Learning for Agent Safety

arXiv:2606. 04051v1 Announce Type: cross Abstract: The evolution of LLMs into tool-enabled agents creates a new class of safety challenges associated with real-world execution rather than simple text generation.

By Xian Qi Loye, Qinglin Su, Zhexin Zhang, Shiyao Cui, Qi Zhu, Fei Mi, Hongning Wang, Minlie Huang

llms agents reinforcement-learning benchmarks safety

arXiv AI

Aug 11

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning

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

llms reinforcement-learning benchmarks

arXiv AI

Jun 26

Joint Learning of Experiential Rules and Policies for Large Language Model Agents

arXiv:2606. 27136v1 Announce Type: new Abstract: For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience.

By Shicheng Ye, Chao Yu

llms agents

Related stories

EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning

COMAP: Co-Evolving World Models and Agent Policies for LLM Agents

TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents

RUBAS: Rubric-Based Reinforcement Learning for Agent Safety

RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning

Joint Learning of Experiential Rules and Policies for Large Language Model Agents