Reinforcement learning

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

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arXiv AI

Aug 10

Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration

arXiv:2608. 06381v1 Announce Type: cross Abstract: Explainable AI (XAI) has shown promise for human-agent collaboration, yet results rely on hand-crafted policies in custom environments, limiting generalizability to state-of-the-art teaming research.

By Mateus Levi Sim\~oes Fernandes, Alberto Sardinha

agents reinforcement-learning benchmarks

arXiv AI

Aug 10

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.

By Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin

llms reinforcement-learning safety

arXiv Machine Learning

Aug 10

Symbolic Graphics Programming with Large Language Models

arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.

By Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu

llms reinforcement-learning multimodal benchmarks

arXiv AI

Aug 10

Flowing Through States: Neural ODE Regularization for Reinforcement Learning

arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.

By Mohamed Ghanem, Bernd Finkbeiner

rag agents reinforcement-learning benchmarks

arXiv AI

Aug 10

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.

By Valentin Li\'{e}vin, Samuel Schmidgall, Tim Strother, Alex Bijamov, Akshay Goel, Anil Palepu, Chunjong Park, Vahid Balazadeh, Min Woo Sun, Marius Guerard, Justin Chen, Dave Steiner, Vikram Dhillon, Ibrahim Azar, Akhil Mehta, Nicholas Spetsieris, Shilpan Shah, Maen Abdelrahim, Amit Dahiya, Yun Liu, Katherine Chou, Yossi Matias, Avinatan Hassidim, Dale R. Webster, Quoc V. Le, Raia Hadsell, Joelle Barral, Carey Radebaugh, Aleksandra Faust, Shekoofeh Azizi, Mike Schaekermann, Po-Hsuan Cameron Chen, Tao Tu, David Racz, Lin Yang

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 10

Learning Fault-Tolerant Locomotion with Adaptive Gait Timing

arXiv:2608. 07328v1 Announce Type: cross Abstract: Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility.

By Giovanbattista Gravina, Luca Rossini, Carlo Rizzardo, Arturo Laurenzi, Nikos Tsagarakis

reinforcement-learning robotics safety

arXiv AI

Aug 10

Beyond Routing Weights: Faithful Response-Level Interpretation of Mixture-of-Experts Reward Models via Contribution Contrast

arXiv:2608. 06400v1 Announce Type: new Abstract: Reward models are central to learning from human preferences, yet identifying what drives their predictions remains challenging.

By Yifan Wang, Jinyi Mu, Mayank Jobanputra, Yu Wang, Soyoung Oh, Isabel Valera, Vera Demberg

reinforcement-learning safety

arXiv AI

Aug 10

Counterfactual Simulation Training for Chain-of-Thought Faithfulness

arXiv:2602. 20710v2 Announce Type: replace Abstract: Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output.

By Peter Hase, Christopher Potts

llms reinforcement-learning

arXiv AI

Aug 10

Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control

arXiv:2608. 07086v1 Announce Type: cross Abstract: Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many tightly coupled factors.

By Qi Zhao, Guozheng Ma, Yilun Kong, Lu Li, Haoyu Wang, Zilin Wang, Tiantian Zhang, Yuxing Wang, Jian Sha, Yongzhe Chang, Xueqian Wang, Dacheng Tao

agents reinforcement-learning benchmarks

arXiv AI

Aug 10

Minimal Ingredients for Reward Assignment from Expert Demonstrations

arXiv:2506. 06793v2 Announce Type: replace-cross Abstract: Reward assignment from scarce demonstrations is a key challenge in both offline and online imitation learning.

By Zixuan Dong, Yumi Omori, Keith Ross

reinforcement-learning benchmarks safety

arXiv AI

Aug 10

Momba: Network Modernization Improves Multi-Objective Reinforcement Learning

arXiv:2608. 07180v1 Announce Type: cross Abstract: Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample efficiency and asymptotic performance without altering the underlying algorithms.

By Adam \v{S}tafa, Santeri Heiskanen, Petr Novotn\'y, Joni Pajarinen

reinforcement-learning benchmarks

arXiv AI

Aug 10

CASA: Classification Augmented with Safety Attention for Robust Multimodal Alignment

arXiv:2604. 00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions.

By Anurag Kumar, Raghuveer Peri, Jon Burnsky, Alexandru Nelus, Rohit Paturi, Srikanth Vishnubhotla, Yanjun Qi

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Aug 10

CrystalGRPO: Target-Aligned and Coverage-Preserving Reinforcement Learning for Flow-Based Crystal Structure Prediction

arXiv:2608. 06582v1 Announce Type: new Abstract: Flow-based generative models can efficiently produce candidate structures for crystal structure prediction (CSP), but their pretrained objectives do not directly optimize downstream target recovery.

By Kaixiang Su, Hongfei Xue, Qiang Zhu

reinforcement-learning

arXiv Machine Learning

Aug 10

AsyncWebRL: Efficient Asynchronous Reinforcement Learning for Multi-Step Visual Web Agents

arXiv:2606. 05597v3 Announce Type: replace Abstract: Training vision-language web agents with multi-step RL is compute-intensive, with two dominant forms of inefficiency: idle GPUs in synchronous RL, and trajectories that use more steps and tokens than necessary.

By Hao Bai, Rui Yang, Chenlu Ye, Spencer Whitehead, Aviral Kumar, Tong Zhang

agents reinforcement-learning efficiency multimodal

arXiv Machine Learning

Aug 10

Sub-Quadratic Bisimulation Metrics via Approximate Nearest Neighbors: Coverage-Augmented Guarantees and Computable Two-Sided Certificates

arXiv:2608. 06762v1 Announce Type: new Abstract: Bisimulation metrics quantify behavioral similarity in Markov decision processes, but their Wasserstein fixed-point operator updates every state pair and incurs quadratic pairwise work.

By Ibne Farabi Shihab, Joyanta Jyoti Mondal

rag reinforcement-learning benchmarks

arXiv Machine Learning

Aug 10

Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks

arXiv:2608. 06520v1 Announce Type: new Abstract: We study online cooperative control of a multi-agent system under Byzantine attacks.

By Ximing Sun, Yue Wang

agents reinforcement-learning

arXiv AI

Aug 10

Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing

arXiv:2608. 07437v1 Announce Type: new Abstract: Reliable hypothesis testing is the foundation of many empirical scientific claims.

By Jiacheng Miao, Jin Mu, Guanhua Chen, James Zou

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 10

Self-Distillation Enables Continual Learning

arXiv:2601. 19897v2 Announce Type: replace Abstract: Continual learning, enabling models to acquire new skills and knowledge without degrading existing capabilities, remains a fundamental challenge for foundation models.

By Idan Shenfeld, Mehul Damani, Jonas H\"ubotter, Pulkit Agrawal

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Aug 10

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

arXiv:2608. 06545v1 Announce Type: new Abstract: Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty.

By Yuepeng Yang, Yuxin Chen, Yuejie Chi

reinforcement-learning safety

arXiv Machine Learning

Aug 10

Online Conformal Prediction Beyond Feedback

arXiv:2608. 07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications.

By Joar Skalse, Edoardo Pona, Osvaldo Simeone, Nicola Paoletti

reinforcement-learning safety