Reinforcement learning

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

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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

arXiv AI

Oct 1

ChronoGraph: Functional 4D Scene Graphs with Vision-Language Models for Interaction Understanding and Grounded Planning

arXiv:2609.39665v1 Announce Type: new Abstract: Embodied agents must determine where to act, anticipate the resulting scene changes, and interpret observed outcomes to guide subsequent actions. This...

By Chenyangguang Zhang, Malgorzata Gwiazda, Guanlong Jiao, Yuanchen Ju, Federico Tombari, Koushil Sreenath, Marc Pollefeys, Sunghwan Hong

llms agents reinforcement-learning robotics fine-tuning multimodal

arXiv AI

Oct 1

ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning

arXiv:2609.38409v1 Announce Type: new Abstract: Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable r...

By \.Ibrahim Ethem Deveci, Funda Tan \c{C}al{\i}k, Bar{\i}\c{s} Deniz Sa\u{g}lam, Duygu Ataman

llms reinforcement-learning benchmarks

arXiv AI

Oct 1

Video Understanding Reward Modeling: A Robust Benchmark and Performant Reward Models

arXiv:2605.07872v2 Announce Type: replace-cross Abstract: Multimodal reward models have advanced substantially in text and image domains, yet progress in video understanding reward modeling remains s...

By Yuancheng Wei, Linli Yao, Lei Li, Haojie Zhang, Hao Zhou, Fandong Meng, Xu Sun

reinforcement-learning multimodal benchmarks

arXiv AI

Oct 1

Code to Control: Synthesizing Parameterized Reactive Controllers

arXiv:2609.38733v1 Announce Type: new Abstract: Recent LLM-based approaches to control either invoke a language model to select actions or synthesize world models that require planning at every decis...

By Zergham Ahmed, Joshua B. Tenenbaum, Chris Bates, Samuel J. Gershman

llms reinforcement-learning robotics

arXiv AI

Oct 1

Loop-Free Inverse Reinforcement Learning via Sequential Value Recovery with Q-Score Matching

arXiv:2609.38955v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) aims to recover a reward function that explains expert demonstrations. Existing IRL methods typically rely on a...

By Yang chen, Yitan Zhang, Michael Witbrock, Shuyue Hu

diffusion reinforcement-learning benchmarks

arXiv AI

Oct 1

Improving OCR Faithfulness via Gated and Attenuated On-Policy Distillation

arXiv:2609.38282v1 Announce Type: new Abstract: Vision-language models may rewrite anomalous text in images into linguistically plausible expressions, compromising OCR transcription faithfulness. Seq...

By Baode Wang, Zuming Huang, Kexuan Ren, Jun Huang, Wei Chu

llms computer-vision reinforcement-learning efficiency multimodal

arXiv AI

Oct 1

MoFlow: Multi-Objective Agentic Workflow Generation

arXiv:2609.38294v1 Announce Type: new Abstract: We study the generation of agentic workflows that jointly optimize multiple objectives, such as accuracy, cost, latency, robustness, and consistency. E...

By Yining Lu, Aurelie Lozano, Xi Yang, Naoki Abe, Yu Deng, Meng Jiang

agents nlp reinforcement-learning benchmarks

arXiv AI

Oct 1

OpenJev-RLCD: A Working RLCD Implementation

arXiv:2609.38850v1 Announce Type: new Abstract: Decision models such as Jev answer questions with probabilities, which are only useful if they are calibrated. Open-source reproductions rely on superv...

By Zhimin Gao, Pichao Wang

reinforcement-learning fine-tuning

arXiv AI

Oct 1

Reinforcing Multimodal Reasoning via Token-Level Perception-Grounded Advantage Estimation

arXiv:2609.39168v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing...

By Zhihan Zhang, Lizi Liao

llms reinforcement-learning multimodal benchmarks

arXiv AI

Oct 1

EHR-RobustGym: Benchmarking and Training Agents for Robust Clinical Reasoning

arXiv:2609.39371v1 Announce Type: new Abstract: In hospital workflows, electronic health records (EHRs) are often noisy, and may not contain the evidence needed to confirm events or measurements refe...

By Yitong Qiao, Yancheng Jin, Lei Liu, Yue Shen, Jian Wang, Jinjie Gu, Zhixuan Chu

llms agents reinforcement-learning fine-tuning benchmarks data-engineering

arXiv AI

Oct 1

Unlearnable, or Unmeasured? On the Reliability of Difficulty Labels in RLVR

arXiv:2609.40115v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become an important approach for improving reasoning during post-training. Recent work sugges...

By Chandak Chakma, Syed Nazmus Sakib, Nafiul Haque, Shifat E. Arman

reinforcement-learning

arXiv AI

Oct 1

Searching for BSM Experimental Signatures with Large Lagrangian Models

arXiv:2609.38309v1 Announce Type: cross Abstract: The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminatin...

By Ibrahim Elsharkawy, Victoria Knapp-Perez, Wahid Bhimji, Aishik Ghosh

llms agents reinforcement-learning fine-tuning

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