"sample efficiency" Papers
72 papers found • Page 1 of 2
Conference
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees
Sangwoo Park, Matteo Zecchin, Osvaldo Simeone
A Differential and Pointwise Control Approach to Reinforcement Learning
Minh Nguyen, Chandrajit Bajaj
Avoiding exp(R) scaling in RLHF through Preference-based Exploration
Mingyu Chen, Yiding Chen, Wen Sun et al.
BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models
Peiyan Li, Yixiang Chen, Hongtao Wu et al.
Causal Information Prioritization for Efficient Reinforcement Learning
Hongye Cao, Fan Feng, Tianpei Yang et al.
Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering
Klaus-Rudolf Kladny, Bernhard Schölkopf, Michael Muehlebach
Contrastive Representation for Interactive Recommendation
Jingyu Li, Zhiyong Feng, Dongxiao He et al.
Direct Alignment with Heterogeneous Preferences
Ali Shirali, Arash Nasr-Esfahany, Abdullah Alomar et al.
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Wenlong Wang, Ivana Dusparic, Yucheng Shi et al.
DyMoDreamer: World Modeling with Dynamic Modulation
Boxuan Zhang, Runqing Wang, Wei Xiao et al.
EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling
Jia-Hua Lee, Bor-Jiun Lin, Wei-Fang Sun et al.
Efficient Multi-Policy Evaluation for Reinforcement Learning
Shuze Daniel Liu, Claire Chen, Shangtong Zhang
Efficient Reinforcement Learning with Large Language Model Priors
Xue Yan, Yan Song, Xidong Feng et al.
Flow Equivariant Recurrent Neural Networks
Andy Keller
GLAM: Global-Local Variation Awareness in Mamba-based World Model
Qian He, Wenqi Liang, Chunhui Hao et al.
Investigating Relational State Abstraction in Collaborative MARL
Sharlin Utke, Jeremie Houssineau, Giovanni Montana
JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data
Runjian Chen, Wenqi Shao, Bo Zhang et al.
Kernel Learning for Sample Constrained Black-Box Optimization
Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
Learning (Approximately) Equivariant Networks via Constrained Optimization
Andrei Manolache, Luiz Chamon, Mathias Niepert
Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning
Oleh Kolner, Thomas Ortner, Stanisław Woźniak et al.
Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning
Chenjie Hao, Weyl Lu, Yifan Xu et al.
ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding
Indraneil Paul, Haoyi Yang, Goran Glavaš et al.
Off-policy Reinforcement Learning with Model-based Exploration Augmentation
Likun Wang, Xiangteng Zhang, Yinuo Wang et al.
On scalable and efficient training of diffusion samplers
Minkyu Kim, Kiyoung Seong, Dongyeop Woo et al.
PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment
Daiwei Chen, Yi Chen, Aniket Rege et al.
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learning
Ziheng Cheng, Tianyu Xie, Shiyue Zhang et al.
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
Grace Zhang, Ayush Jain, Injune Hwang et al.
ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
Timo Kaufmann, Yannick Metz, Daniel Keim et al.
Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts
Minh Le, Chau Nguyen, Huy Nguyen et al.
Safety-Prioritizing Curricula for Constrained Reinforcement Learning
Cevahir Koprulu, Thiago Simão, Nils Jansen et al.
Sample- and Parameter-Efficient Auto-Regressive Image Models
Elad Amrani, Leonid Karlinsky, Alex M. Bronstein
Sample-Efficient Multi-Round Generative Data Augmentation for Long-Tail Instance Segmentation
Byunghyun Kim, Minyoung Bae, Jae-Gil Lee
Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
Daniel Palenicek, Florian Vogt, Joe Watson et al.
ShiQ: Bringing back Bellman to LLMs
Pierre Clavier, Nathan Grinsztajn, Raphaël Avalos et al.
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
Samuel Garcin, Trevor McInroe, Pablo Samuel Castro et al.
Succeed or Learn Slowly: Sample Efficient Off-Policy Reinforcement Learning for Mobile App Control
Georgios Papoudakis, Thomas Coste, Jianye Hao et al.
Thompson Sampling in Function Spaces via Neural Operators
Rafael Oliveira, Xuesong Wang, Kian Ming Chai et al.
Time Reversal Symmetry for Efficient Robotic Manipulations in Deep Reinforcement Learning
Yunpeng Jiang, Jianshu Hu, Paul Weng et al.
Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound
Tal Fiskus, Uri Shaham
When Should We Prefer State-to-Visual DAgger over Visual Reinforcement Learning?
Tongzhou Mu, Zhaoyang Li, Stanisław Wiktor Strzelecki et al.
Advancing DRL Agents in Commercial Fighting Games: Training, Integration, and Agent-Human Alignment
Chen Zhang, Qiang HE, Yuan Zhou et al.
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback
Kihyun Kim, Jiawei Zhang, Asuman Ozdaglar et al.
Better & Faster Large Language Models via Multi-token Prediction
Fabian Gloeckle, Badr Youbi Idrissi, Baptiste Roziere et al.
Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
Zizhao Wang, Caroline Wang, Xuesu Xiao et al.
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran, Mohamad H Danesh, Stefano Albrecht et al.
Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Michelle Pan, Mariah Schrum, Vivek Myers et al.
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
Hany Hamed, Subin Kim, Dongyeong Kim et al.
EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data
Shengjie Wang, Shaohuai Liu, Weirui Ye et al.
Episodic Return Decomposition by Difference of Implicitly Assigned Sub-trajectory Reward
Haoxin Lin, Hongqiu Wu, Jiaji Zhang et al.
Feasible Reachable Policy Iteration
Shentao Qin, Yujie Yang, Yao Mu et al.