arXiv Machine Learning

Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia

arXiv:2607. 06626v1 Announce Type: new Abstract: Recent Vision-Language Models capture increasingly complex aspects of human cognition.

arXiv Machine Learning

Aug 6

Reward Structure Shapes the Interaction Between Episodic Exploration and Neural Memory in Reinforcement Learning

arXiv:2608. 05111v1 Announce Type: new Abstract: In partially observable reinforcement learning, agents face a dual bottleneck: they must explore to encounter rewarding states and retain that experience in memory to optimize their policies.

By Jai Malegaonkar, Rohan Patil, Henrik I. Christensen

agents reinforcement-learning efficiency

Hugging Face Trending Papers

Jun 3

Trace-Mediated Peak Bias: Bridging Temporal Credit Assignment and Cognitive Heuristics in Deep Reinforcement Learning

Temporal credit assignment is central to both biological and artificial intelligence, yet its interaction with non-linear function approximation is poorly understood. We identify a systematic failure mode in deep reinforcement learning (RL) termed Trace-Mediated Peak Bias (TMPB).

agents reinforcement-learning safety

arXiv AI

Aug 6

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning

arXiv:2608. 04663v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often adds social terms to individual rewards, yet the scale of those terms is usually chosen by hand.

By Aaditya Mehta, Arya Shah

agents reinforcement-learning

arXiv AI

Jun 4

Trace-Mediated Peak Bias: Bridging Temporal Credit Assignment and Cognitive Heuristics in Deep Reinforcement Learning

arXiv:2606. 04735v1 Announce Type: cross Abstract: Temporal credit assignment is central to both biological and artificial intelligence, yet its interaction with non-linear function approximation is poorly understood.

By Viktor Vesel\'y, Aleksandar Todorov, Erwan Escudie, Matthia Sabatelli

agents reinforcement-learning safety

arXiv AI

Aug 5

Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning

arXiv:2608. 03875v1 Announce Type: cross Abstract: Designing effective reward functions remains a major bottleneck in Reinforcement Learning (RL).

By Pyrros Koussios, Chenhao Li, Xin Chen, Andreas Krause

llms diffusion reinforcement-learning fine-tuning multimodal safety

arXiv AI

Jul 7

Attention Limited Reward Learning

arXiv:2607. 04590v1 Announce Type: new Abstract: Pairwise human comparisons are a primary interface through which modern AI systems learn human preferences.

By Wenqian Xing

reinforcement-learning safety

arXiv Machine Learning

Jun 25

Reward-Conditioned Attention: How Reward Design Shapes What Autonomous Driving Agents See

arXiv:2606. 25127v1 Announce Type: new Abstract: We investigate how reward design shapes the internal attention patterns of reinforcement learning agents trained for autonomous driving.

By Mohamed Benabdelouahad, Ahmed Djalal Hacini, Nadir Farhi, Aissa Boulmerka

agents reinforcement-learning robotics safety

arXiv AI

Jul 24

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

arXiv:2602. 19313v2 Announce Type: replace-cross Abstract: General-purpose robot learning requires dense, instruction-conditioned feedback that can distinguish meaningful task progress from stalled, failed, or partially completed behavior.

By Shirui Chen, Cole Harrison, Ying-Chun Lee, Angela Jin Yang, Zhongzheng Ren, Lillian J. Ratliff, Jiafei Duan, Dieter Fox, Ranjay Krishna

llms robotics multimodal benchmarks

arXiv AI

Jul 22

Measuring Reward-Seeking via Contrastive Belief Updates

arXiv:2607. 18966v1 Announce Type: new Abstract: Language models trained with reinforcement learning may learn to optimize the grader's judgment rather than the intended objective.

By Axel H{\o}jmark, J\'er\'emy Scheurer, Evgenia Nitishinskaya, Felix Hofst\"atter, Jason Wolfe, Theodore Ehrenborg, Bronson Schoen, Alexander Meinke

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 2

NeuroCogMap Reveals Cognitive Organization of Large Language Models

arXiv:2607. 00397v1 Announce Type: cross Abstract: Understanding how complex cognitive functions are organized within artificial systems is central to interpreting large language models (LLMs) and relating them to biological cognition.

By Zhongxiang Sun, Haolang Lu, Qiang Ma, Qi Li, Qipeng Wang, Liang Pang, Chenyu Liu, Qiankun Li, Hao Sun, Kun Wang, Yi Zeng, Jun Xu, Guoqi Li, Ji-Rong Wen

llms safety

arXiv AI

Aug 11

TrustRoboReward: Preference-Ordered Isotonic Score Editing for Multi-Paradigm Robot Reward Models

arXiv:2608. 08491v1 Announce Type: new Abstract: Reward models are a bottleneck for reinforcement learning in embodied AI.

By Yidong Wang, Yan Zhan, Ziteng Feng, Zhenyu Cui, Ziyi Zhou, Renzhao Liang, Jiaxuan Zhu, Zilei Yang, Yiran Zhao, Zhongkuan Mao, Bo Jia, Hanchu Ni, Chenggang Xie, Biao Liu, Yi Zhang, Yong Dai, Xiaozhu Ju, Wei Ye, Shikun Zhang

llms reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jul 10

A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents

arXiv:2607. 07753v1 Announce Type: cross Abstract: Modelling psychological disorders in artificial agents offers both a testbed for computational psychiatry and a lens on the failure modes of affective control.

By Hari Prasad

agents reinforcement-learning robotics

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