arXiv AI

ReCal: Reward Calibration for RL-based LLM Routing

arXiv:2606. 12479v1 Announce Type: cross Abstract: Large language model (LLM) routing has emerged as an effective paradigm for leveraging the complementary strengths of multiple LLMs through dynamic model and reasoning-strategy selection.

arXiv AI

Jun 2

PR2: Predictive Routing Replay for MoE-Based LLM Reinforcement Learning

arXiv:2606. 00395v1 Announce Type: cross Abstract: Mixture of Experts (MoE) Large Language Models (LLMs) achieve strong performance at scale.

By Daize Dong, Junlin Chen, Haolong Jia, Jiawei Wu, Huanwei Di, Jiang Liu, Jialian Wu, Zhengzhong Liu, Zicheng Liu, Emad Barsoum, Dimitris N. Metaxas, Hongyi Wang

llms reinforcement-learning benchmarks

arXiv AI

Jun 16

STRIDE: Strategic Trajectory Reasoning via Discriminative Estimation for Verifiable Reinforcement Learning

arXiv:2606. 15866v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training paradigm for improving the reasoning abilities of large language models.

By Qinjian Zhao, Zhihao Dou, Dinggen Zhang, Xiangyu Li, Chaoda Song, Zhongwei Wan, Xinpeng Li, Yanyan Zhang, Kaijie Chen, Qingtao Pan, Chengcheng Feng, Zhiqiang Gao, Xiaoyu Xia

llms agents reinforcement-learning

arXiv AI

Jul 23

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

arXiv:2601. 06487v3 Announce Type: replace-cross Abstract: Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on open-ended agent tasks with vast solution spaces (e.

By Qiang Zhang, Boli Chen, Fanrui Zhang, Ruixue Ding, Shihang Wang, Qiuchen Wang, Yinfeng Huang, Haonan Zhang, Rongxiang Zhu, Pengyong Wang, Ailin Ren, Xin Li, Pengjun Xie, Jiawei Liu, Ning Guo, Jingren Zhou, Zheng-Jun Zha

llms agents reinforcement-learning fine-tuning benchmarks safety

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

Jul 7

RSPO: Reward-Swap Policy Optimization for Multi-Turn LLM Agents

arXiv:2607. 04713v1 Announce Type: cross Abstract: Reinforcement learning holds significant potential for training large language models (LLMs) to handle multi-turn interactive tasks.

By Qiang Liu, Taian Guo, Ruizhi Qiao, Xing Sun

llms agents reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Aug 12

Reinforcement Learning-based Semi-supervised Knowledge Distillation with LLM-as-a-Judge

arXiv:2604. 02621v2 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) substantially improves the reasoning capabilities of language models, but most existing RL fine-tuning approaches rely entirely on ground-truth verifiable rewards and thus labeled datasets with verifiable answers.

By Yiyang Shen, Lifu Tu, Weiran Wang

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jul 9

UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma

arXiv:2607. 06987v1 Announce Type: new Abstract: Reinforcement learning (RL) has become the standard paradigm for enhancing the complex reasoning capabilities of large language models (LLMs).

By Chongyu Fan, Pengfei Liu, Jingjia Huang, Sijia Liu, Yi Lin

llms reinforcement-learning multimodal

arXiv AI

Jul 24

EmoAgent-R1: Towards Multimodal Emotion Understanding with Reinforcement Learning-based Dynamic Agent Specialization

arXiv:2607. 21013v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER to a new level that is complex emotion understanding with advanced video understanding abilities and natural language description.

By Lihuang Fang, Yuchen Zou, kebin Jin, Jinghui Qin

llms agents reinforcement-learning multimodal benchmarks

arXiv AI

Jun 9

Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short

arXiv:2606. 09380v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a leading paradigm for improving the reasoning ability of large language models through outcome-based supervision.

By Han Zhou, Adam X. Yang, Laurence Aitchison, Anna Korhonen, Albert Q. Jiang

llms 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

arXiv AI

Jun 2

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).

By Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Aug 11

Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training

arXiv:2608. 09217v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute without regard to differences in how tasks respond to optimization.

By Ting Zhou, Zhenqing Ling, Daoyuan Chen, Qianli Shen, Yilun Huang, Ying Shen, Yaliang Li

llms reinforcement-learning benchmarks

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