Zhang-Wei Hong

According to our database, Zhang-Wei Hong authored at least 51 papers between 2017 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

Vector Policy Optimization: Training for Diversity Improves Test-Time Search.

Ryan Bahlous-Boldi, Isha Puri, Idan Shenfeld, Akarsh Kumar, Mehul Damani, Sebastian Risi, Omar Khattab, Zhang-Wei Hong, Pulkit Agrawal

CoRR, May, 2026

FlowCompile: An Optimizing Compiler for Structured LLM Workflows.

Junyan Li, Zhang-Wei Hong, Maohao Shen, Yang Zhang, Chuang Gan

CoRR, May, 2026

A Reward-Free Viewpoint on Multi-Objective Reinforcement Learning.

Ying-Tu Chen, Wei Hung, Bing-Shu Wu, Zhang-Wei Hong, Ping-Chun Hsieh

CoRR, April, 2026

Decocted Experience Improves Test-Time Inference in LLM Agents.

Maohao Shen, Kaiwen Zha, Zexue He, Zhang-Wei Hong, Siru Ouyang, J. Jon Ryu, Prasanna Sattigeri, Suhas N. Diggavi, Gregory W. Wornell

CoRR, April, 2026

Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization.

Yihang Yao, Zhepeng Cen, Haohong Lin, Shiqi Liu, Zuxin Liu, Jiacheng Zhu, Zhang-Wei Hong, Laixi Shi, Ding Zhao

CoRR, February, 2026

Tailored Primitive Initialization is the Secret Key to Reinforcement Learning.

Yihang Yao, Guangtao Zeng, Raina Wu, Yang Zhang, Ding Zhao, Zhang-Wei Hong, Chuang Gan

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026

2025

BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization.

Iris Xu, Guangtao Zeng, Zexue He, Charles Jin, Aldo Pareja, Dan Gutfreund, Chuang Gan, Zhang-Wei Hong

CoRR, December, 2025

GPTOpt: Towards Efficient LLM-Based Black-Box Optimization.

Jamison Meindl, Yunsheng Tian, Tony Cui, Veronika Thost, Zhang-Wei Hong, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic

CoRR, October, 2025

Composition-Grounded Instruction Synthesis for Visual Reasoning.

Xinyi Gu, Jiayuan Mao, Zhang-Wei Hong, Zhuoran Yu, Pengyuan Li, Dhiraj Joshi, Rogério Feris, Zexue He

CoRR, October, 2025

ZeroShotOpt: Towards Zero-Shot Pretrained Models for Efficient Black-Box Optimization.

Jamison Meindl, Yunsheng Tian, Tony Cui, Veronika Thost, Zhang-Wei Hong, Johannes Dürholt, Jie Chen, Wojciech Matusik, Mina Konakovic-Lukovic

CoRR, October, 2025

Your Reward Function for RL is Your Best PRM for Search: Unifying RL and Search-Based TTS.

Can Jin, Yang Zhou, Qixin Zhang, Hongwu Peng, Di Zhang, Marco Pavone, Ligong Han, Zhang-Wei Hong, Tong Che, Dimitris N. Metaxas

CoRR, August, 2025

Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering.

Guangtao Zeng, Maohao Shen, Delin Chen, Zhenting Qi, Subhro Das, Dan Gutfreund, David Cox, Gregory W. Wornell, Wei Lu, Zhang-Wei Hong, Chuang Gan

CoRR, May, 2025

A distributional reinforcement learning model for optimal glucose control after cardiac surgery.

Jacob Desman, Zhang-Wei Hong, Moein Sabounchi, Ashwin S. Sawant, Jaskirat Gill, Ana C. Costa, Gagan Kumar, Rajeev Sharma, Arpeta Gupta, Paul McCarthy, Veena Nandwani, Doug Powell, Alexandra Carideo, Donnie Goodwin, Sanam Ahmed, Umesh Gidwani, Matthew A. Levin, Robin Varghese, Farzan Filsoufi, Robert Freeman, Avniel Shetreat-Klein, Alexander W. Charney, Ira S. Hofer, Lili Chan, David Reich, Patricia H. Kovatch, Roopa Kohli-Seth, Monica Kraft, Pulkit Agrawal, John A. Kellum, Girish N. Nadkarni, Ankit Sakhuja

npj Digit. Medicine, 2025

RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning.

Kaiwen Zha, Zhengqi Gao, Maohao Shen, Zhang-Wei Hong, Duane S. Boning, Dina Katabi

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search.

Maohao Shen, Guangtao Zeng, Zhenting Qi, Zhang-Wei Hong, Zhenfang Chen, Wei Lu, Gregory W. Wornell, Subhro Das, David Daniel Cox, Chuang Gan

Proceedings of the Forty-second International Conference on Machine Learning, 2025

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization.

Chen Bo Calvin Zhang, Zhang-Wei Hong, Aldo Pacchiano, Pulkit Agrawal

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

ReGen: Generative Robot Simulation via Inverse Design.

Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Erfan Aasi, Andrew Silva, Guy Rosman, Sertac Karaman, Daniela Rus

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Composing Synergistic Macro Actions for Reinforcement Learning Agents.

Yu-Ming Chen, Kuan-Yu Chang, Chien Liu, Tsu-Ching Hsiao, Zhang-Wei Hong, Chun-Yi Lee

IEEE Trans. Neural Networks Learn. Syst., May, 2024

Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building.

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R. Fiete

Trans. Mach. Learn. Res., 2024

Embodied Red Teaming for Auditing Robotic Foundation Models.

Sathwik Karnik, Zhang-Wei Hong, Nishant Abhangi, Yen-Chen Lin, Tsun-Hsuan Wang, Pulkit Agrawal

CoRR, 2024

ImageNet-RIB Benchmark: Large Pre-Training Datasets Don't Guarantee Robustness after Fine-Tuning.

Jaedong Hwang, Brian Cheung, Zhang-Wei Hong, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete

CoRR, 2024

ROER: Regularized Optimal Experience Replay.

Changling Li, Zhang-Wei Hong, Pulkit Agrawal, Divyansh Garg, Joni Pajarinen

RLJ, 2024

Going Beyond Heuristics by Imposing Policy Improvement as a Constraint.

Chi-Chang Lee, Zhang-Wei Hong, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models.

Phat Nguyen, Tsun-Hsuan Wang, Zhang-Wei Hong, Sertac Karaman, Daniela Rus

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024

Maximizing Quadruped Velocity by Minimizing Energy.

Srinath Mahankali, Chi-Chang Lee, Gabriel B. Margolis, Zhang-Wei Hong, Pulkit Agrawal

Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Random Latent Exploration for Deep Reinforcement Learning.

Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Curiosity-driven Red-teaming for Large Language Models.

Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James R. Glass, Akash Srivastava, Pulkit Agrawal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity.

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete

CoRR, 2023

Neuro-Inspired Efficient Map Building via Fragmentation and Recall.

Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila Fiete

CoRR, 2023

Decentralized Inference via Capability Type Structures in Cooperative Multi-Agent Systems.

Charles Jin, Zhang-Wei Hong, Farid Arthaud, Idan Orzech, Martin C. Rinard

CoRR, 2023

Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets.

Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik, Abhishek Bhandwaldar, Akash Srivastava, Joni Pajarinen, Romain Laroche, Abhishek Gupta, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Model Predictive Control via On-Policy Imitation Learning.

Kwangjun Ahn, Zakaria Mhammedi, Horia Mania, Zhang-Wei Hong, Ali Jadbabaie

Proceedings of the Learning for Dynamics and Control Conference, 2023

TGRL: An Algorithm for Teacher Guided Reinforcement Learning.

Idan Shenfeld, Zhang-Wei Hong, Aviv Tamar, Pulkit Agrawal

Proceedings of the International Conference on Machine Learning, 2023

Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation.

Zechu Li, Tao Chen, Zhang-Wei Hong, Anurag Ajay, Pulkit Agrawal

Proceedings of the International Conference on Machine Learning, 2023

Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting.

Zhang-Wei Hong, Pulkit Agrawal, Remi Tachet des Combes, Romain Laroche

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Bilinear value networks.

Zhang-Wei Hong, Ge Yang, Pulkit Agrawal

CoRR, 2022

Redeeming intrinsic rewards via constrained optimization.

Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Stubborn: A Strong Baseline for Indoor Object Navigation.

Haokuan Luo, Albert Yue, Zhang-Wei Hong, Pulkit Agrawal

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Bi-linear Value Networks for Multi-goal Reinforcement Learning.

Zhang-Wei Hong, Ge Yang, Pulkit Agrawal

Proceedings of the Tenth International Conference on Learning Representations, 2022

Topological Experience Replay.

Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Periodic Intra-ensemble Knowledge Distillation for Reinforcement Learning.

Zhang-Wei Hong, Prabhat Nagarajan, Guilherme Maeda

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture.

Chin-Jui Chang, Yu-Wei Chu, Chao-Hsien Ting, Hao-Kang Liu, Zhang-Wei Hong, Chun-Yi Lee

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

2020

Mixture of Step Returns in Bootstrapped DQN.

Po-Han Chiang, Hsuan-Kung Yang, Zhang-Wei Hong, Chun-Yi Lee

CoRR, 2020

2019

Model-based Lookahead Reinforcement Learning.

Zhang-Wei Hong, Joni Pajarinen, Jan Peters

CoRR, 2019

Adversarial Active Exploration for Inverse Dynamics Model Learning.

Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Chun-Yi Lee

Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018

Adversarial Exploration Strategy for Self-Supervised Imitation Learning.

Zhang-Wei Hong, Tsu-Jui Fu, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee

CoRR, 2018

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning.

Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Tsu-Jui Fu, Chun-Yi Lee

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Virtual-to-Real: Learning to Control in Visual Semantic Segmentation.

Zhang-Wei Hong, Yu-Ming Chen, Hsuan-Kung Yang, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Brian Hsi-Lin Ho, Chih-Chieh Tu, Tsu-Ching Hsiao, Hsin-Wei Hsiao, Sih-Pin Lai, Yueh-Chuan Chang, Chun-Yi Lee

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Diversity-Driven Exploration Strategy for Deep Reinforcement Learning.

Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee

Proceedings of the 6th International Conference on Learning Representations, 2018

A Deep Policy Inference Q-Network for Multi-Agent Systems.

Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann, Yi-Hsiang Chang, Chun-Yi Lee

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

2017

Tactics of Adversarial Attack on Deep Reinforcement Learning Agents.

Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun

Proceedings of the 5th International Conference on Learning Representations, 2017