Critic Learning

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 16

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FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting
Oct 1, 2026Kyungmin Lee, Sibeen Kim, Dongyoon Hwang +6Cross-Embodiment Retargeting Dexterous Manipulation
KAIST AI · Holiday Robotics
Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening
Sep 16, 2026Yizhuo Li, Jianhao Yan, Yun Luo +9Proximal Policy Optimization Critic Learning
Shanghai Jiao Tong University · Shanghai AI Laboratory · Westlake University +4
Hybrid Advantage Estimation with Unified Critic for VLM Agentic Reinforcement Learning
Jul 26, 2026Wenxuan Zhang, Yuhui Wang, Donggang Jia +5Agentic Reinforcement Learning Critic Learning
KAUST
Critic Architecture Matters: Dual vs. Unified Critics for Humanoid Loco-Manipulation
Jun 10, 2026Mehmet Turan YardımcıHumanoid Loco-Manipulation Robotic Manipulation Policies
Department of Computer Engineering C¸ukurova University Adana, Türkiye
Critic-Guided Heterogeneous Multi-Agent Reasoning for Reliable Mathematical Problem Solving
Jun 4, 2026Muhammad Talha Sharif, Abdul RehmanMathematical Reasoning LLM Reasoning Strategies
dept. of Computer Science · National University of Computer and Emerging Sciences · Islamabad, Pakistan
Failure Modes of Deep Multi-Agent RL in Asynchronous Pricing: Reproducible Triggers, Trace Diagnostics, and a Partial Fix
Jun 3, 2026Shree Murthy, Rohan PandeyMulti-Agent Reinforcement Learning Dynamic Pricing
DigitalOcean, USA
Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
May 26, 2026Dhruv S. Kushwaha, Zoleikha A. BironControl Barrier Functions Soft Actor-Critic
Refined Analysis of Entropy-Regularized Actor-Critic
May 23, 2026Safwan Labbi, Paul Mangold, Daniil Tiapkin +1Entropy Regularized Reinforcement Learning Soft Actor-Critic
CMAP, CNRS, École Polytechnique, Institut Polytechnique de Paris, 91120 Palaiseau, France · Université Paris-Saclay, CNRS, LMO, 91405, Orsay, France · Google DeepMind +2
Reinforcement Learning-based Control via Y-wise Affine Neural Networks: Comparative Case Studies for Chemical Processes
May 20, 2026Austin Braniff, Yuhe TianChemical Process Systems Model Predictive Control
Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 26506 USA
CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering
May 18, 2026Mahesh Bhosale, Abdul Wasi, Vishvesh Trivedi +3Long Video Question Answering Keyframe Selection
University at Buffalo · 2New York University
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
May 5, 2026Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh ChangVariational Quantum Circuits Hierarchical Reinforcement Learning
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Wells Fargo, New York, NY, USA · MediaTek Inc., Hsinchu, Taiwan
Temporally Extended Mixture-of-Experts Models
Apr 22, 2026Zeyu Shen, Peter HendersonMixture-Of-Experts Critic Learning
1Princeton University
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Apr 21, 2026Yuan Zhuang, Yuexin Bian, Sihong He +7Critic Learning Off-Policy Learning
University of Connecticut · University of California San Diego · University of Texas at Arlington +1
GAIA: A Data Flywheel System for Training GUI Test-Time Scaling Critic Models
Jan 26, 2026Shaokang Wang, Pei Fu, Ruoceng Zhang +7Graphical User Interface Agents Critic Learning
MiLM Plus, Xiaomi Inc, Beijing, China · School of Computer Science, Peking University, Beijing, China
Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning
Jun 8, 2025Yang Xu, Swetha Ganesh, Vaneet AggarwalQ-Learning Soft Actor-Critic
Purdue University, West Lafayette, Indiana, USA 47907
Diversity-Enriched Option-Critic
Nov 4, 2020Anand Kamat, Doina PrecupCritic Learning Reward Functions
McGill University · Mila · 3Google deepmind

Oct 1, 2026, cs.ROJ/K move · Enter open · S save

FlashDexRetarget: Accelerating Dexterous Manipulation Data Generation through Multi-Motion Retargeting

Kyungmin Lee, Sibeen Kim, Dongyoon Hwang, Yoonsang Oh+5

KAIST AI · Holiday Robotics

Cross-Embodiment Retargeting Dexterous Manipulation Critic Learning Flash

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Human hand-object demonstrations provide a scalable source of data for dexterous robot learning, but transferring them across embodiments requires physically feasible retargeting. Existing physics-based methods typically optimize each demonstration independently, leading to either limited success under finite simulation budgets or training costs that grow with dataset size. We introduce FlashDexRetarget, an RL framework for multi-reference dexterous retargeting. We formulate retargeting as multi-reference tracking, jointly learning a single policy across many demonstrations with off-policy RL and geometric supervision of the demonstrated interactions. This shared training formulation amortizes optimization across references while enabling the policy to track diverse hand-object interactions. On a 50-motion benchmark from TACO, OakInk2, and HOT3D using XHand and Sharpa Wave Hand as target embodiments, FlashDexRetarget retargets 90% of demonstrations using about 30 GPU-hours, compared with about 46% at about 3,000 GPU-hours for CHORD. This corresponds to about 100 times lower training compute and a 44-percentage-point improvement in retargeting success. Ablations examine the key design choices, while experiments with up to 1,000 motions and real-world replay further demonstrate the scalability and practical applicability of our method.

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