Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

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arXiv AI

Oct 1

Semifactual Credit-Augmented Policy Optimization

arXiv:2609.40360v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet their predictions...

By Junshu Pan, Zhizhang Fu, Shulin Huang, Yiran Ding, Zifan Cheng, Wenqi Shao, Qiaosheng Zhang, Yue Zhang

llms reinforcement-learning benchmarks

arXiv AI

Oct 1

Learning What to Practice: Diagnosis-Guided Self-Evolution for Language Models

arXiv:2609.00768v2 Announce Type: replace Abstract: Self-play supports the self-evolution of language models, but solver performance can plateau or decline across rounds without guidance. Existing un...

By Xincheng Wei, Yifan Ding, Fucheng Xiong, Yoshua Li, Dongsheng Ma, Rongxiang Weng, Xunliang Cai, Wenjian Ding, Yao Zhang

llms reinforcement-learning benchmarks

arXiv AI

Oct 1

CompoWorld: Compositional Environment Scaling for General Agents

arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly...

By Xiao-Wen Yang, Weiyi Xu, Wen Da, Hang Xu, Canwei Li, Hong-Jie You, Pusen Dong, Yucheng Zeng, Zhaokai Luo, Yu-Feng Li, Yao Hu, Mu Chuan

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Oct 1

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule

arXiv:2601.18681v3 Announce Type: replace-cross Abstract: We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Un...

By Yilie Huang, Wenpin Tang, Xunyu Zhou

diffusion reinforcement-learning

arXiv AI

Oct 1

TensorHub: Scalable and Elastic Weight Transfer for LLM RL Training

arXiv:2604.09107v2 Announce Type: replace-cross Abstract: Modern LLM reinforcement learning (RL) workloads require a high-performance weight transfer system to scale training across heterogeneous com...

By Chenhao Ye, Huaizheng Zhang, Mingcong Han, Baoquan Zhong, Xiang Li, Qixiang Chen, Xinyi Zhang, Weidong Zhang, Kaihua Jiang, Wang Zhang, He Sun, Wencong Xiao, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau

llms reinforcement-learning

arXiv AI

Oct 1

GrepSeek: Training Search Agents for Direct Corpus Interaction

arXiv:2605.29307v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Mo...

By Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, Razieh Rahimi, Fernando Diaz, Hamed Zamani

llms agents reinforcement-learning benchmarks

arXiv AI

Oct 1

Adaptive Reparametrized Time for Score-Based Diffusion Sampling

arXiv:2607.02137v3 Announce Type: replace-cross Abstract: We study timestep allocation for score-based diffusion sampling, where a learned reverse-time dynamics is discretized on a finite grid. Unifo...

By Yilie Huang, Wenpin Tang, Xun Yu Zhou

diffusion reinforcement-learning

arXiv AI

Oct 1

Coding Agent Memory Post-training: Unlocking the Memory Potential of Pre-trained File Operations for Long-Horizon Tasks via Reinforcement Learning

arXiv:2609.34422v2 Announce Type: replace-cross Abstract: Language-model agents increasingly tackle long-horizon tasks whose interaction histories exceed the model's active context. Recent work has b...

By Lirui Luo, Kelong Mao, Heming Xia, Rongqing Li, Xinwei Yang, Luyu Chen, Kieran Wong, Yudong Guo, Xinrui Wang, Jiayin Zhu, Simiu Gu, Sulong Xu, Cong Fang

agents reinforcement-learning

arXiv Computer Vision

Oct 1

ExploreNet: Learning Where to Explore in Diffusion GRPO

arXiv:2609.38329v1 Announce Type: new Abstract: Group-relative RL methods such as Flow-GRPO post-train image generators by exploring with isotropic Gaussian noise added at every denoising step. This...

By Shuyue Stella Li, Xiaochuang Han, Yulia Tsvetkov, Luke Zettlemoyer

diffusion reinforcement-learning benchmarks

arXiv Computer Vision

Oct 1

MOBA-VL: Event-Localized Multi-Turn Reinforcement Learning for Real-Time MOBA Commentary

arXiv:2609.38428v1 Announce Type: new Abstract: Real-time commentary for Multiplayer Online Battle Arena (MOBA) esports requires a vision-language model (VLM) to narrate a live match second by second...

By Shengyun Zhong, Xinkang Zhao, Ziyuan Chu, Linchao Zhu

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 1

Vision-Language-Action Autonomous Driving Agent with Language-based Memory

arXiv:2609.38641v1 Announce Type: new Abstract: Vision-Language-Action (VLA) foundation models have recently emerged as one of the prevailing solutions for autonomous driving, as they can utilize kno...

By Kai Yan, Xiangyu Chen, Yulong Cao, Alex Naumann, Peter Karkus, Yan Wang, Jef Packer, Alex Schwing, Yuxiong Wang, Boris Ivanovic, Wenjie Luo, Marco Pavone

agents nlp reinforcement-learning robotics fine-tuning multimodal

arXiv AI

Oct 1

Multi-LLM Collaborative Alignment via Stackelberg Games

The paper introduces Stackelberg Alignment, a leader‑follower framework that lets a pool of language models collaborate and improve by learning from each other’s responses. An EXP3 bandit leader adaptively selects instructions based on difficulty and discriminability, while the models act as followers, evaluating peers and learning via DPO or GRPO with Elo‑style reputation weighting and opponent matching. Experiments on diverse benchmarks show that this adaptive curriculum outperforms static baselines by up to 12‑25% and improves multi‑LLM evolution.

By Christina Hahn, Shangbin Feng, Dean Light, Swastik Roy, Hila Gonen, Yulia Tsvetkov

llms reinforcement-learning benchmarks safety

arXiv Computer Vision

Oct 1

From Image Interpretation to Clinical Reasoning: Upstream Physician-Context-Aware Multimodal Learning with Causal Reinforcement Learning

arXiv:2609.38924v1 Announce Type: new Abstract: Major adverse cardiovascular events (MACE) remain the leading cause of mortality worldwide. Opportunistic screening using routinely acquired clinical d...

By Jialu Pi, Yanan Ma, Weijie Chen, Owen Crystal, Shubham Trivedi, Stephen Xie, Anna Silverman, Matthew Stib, Chadi Ayoub, Reza Arsanjani, Imon Banerjee

llms reinforcement-learning efficiency multimodal benchmarks

arXiv Computer Vision

Oct 1

Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection

arXiv:2609.39066v1 Announce Type: new Abstract: Conventional image forgery detection methods produce binary scores or pixel-level masks without interpretable evidence, while recent multimodal large l...

By Zhiya Tan, Jing Huang, Changtao Miao, Lin Tan, Xin Zhang, Weiwei Feng, Jianshu Li, Joey Tianyi Zhou

llms agents reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computer Vision

Oct 1

From Given to Gathered Evidence: Agentic Learning for Longitudinal Medical Reasoning

arXiv:2609.39566v1 Announce Type: new Abstract: Foundation models can serve as clinical agents through tool-use harnesses. However, conventional medical benchmarks assess reasoning over preselected e...

By Minye Shao, Chaohui Yu, Yixuan Wu, Fan Wang, Ling Shao, Yang Long

llms agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Computer Vision

Oct 1

EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection

arXiv:2603.17343v3 Announce Type: replace Abstract: The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Tradition...

By Chenyang Zhu, Maorong Wang, Jun Liu, Ching-Chun Chang, Isao Echizen

llms agents reinforcement-learning multimodal benchmarks safety

arXiv Computer Vision

Oct 1

XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments

arXiv:2604.18484v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from co...

By Kangan Qian, ChuChu Xie, Yang Zhong, Jingrui Pang, Siwen Jiao, Sicong Jiang, Zilin Huang, Yunlong Wang, Kun Jiang, Mengmeng Yang, Hao Ye, Guanghao Zhang, Hangjun Ye, Guang Chen, Long Chen, Diange Yang

llms agents reinforcement-learning robotics multimodal benchmarks

arXiv Computer Vision

Oct 1

Revisit to Segment: Working Memory Distillation for Reasoning Segmentation

arXiv:2609.34863v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have approached image segmentation by reasoning about visual content and predicting target locations. Thei...

By Cilin Yan, Yilun Qiu, Wanyang Zhang, Rui Zu, Xiaolong Jiang, Jiayin Cai, Yao Hu

llms computer-vision reinforcement-learning efficiency multimodal benchmarks

arXiv Statistics ML

Oct 1

Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization

arXiv:2501.18183v3 Announce Type: replace-cross Abstract: We introduce a novel framework for decentralized projection-free optimization, extending projection-free methods to a broader class of upper-...

By Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal

reinforcement-learning

arXiv AI

Oct 1

Free Everywhere, Exact on Trees: PPO's Dropped Correction Buys Sample Efficiency Under Aggressive Reuse

arXiv:2609.39634v1 Announce Type: cross Abstract: Common policy improvement methods, including TRPO, PPO, and GRPO, estimate policy improvement under the behavioral policy's state-visitation distribu...

By Nima H. Siboni

reinforcement-learning safety

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