1 citations · 3 across the 10 of their papers we have counts for
11 papers
RLE-Bench: A Qualifying Exam for Coding Agents as Robot Learning Engineers
Haitong Ma, Chenxiao Gao, Rushi Qiang +2
Coding agents are beginning to move beyond purely digital tasks to tackle physical-world challenges, particularly in robotics. Existing robotics benchmarks, however, primarily focu…
Revisiting DAgger in the Era of LLM-Agents
Changhao Li, Rushi Qiang, Jiawei Huang +4
Long-horizon LM agents learn from multi-turn interaction, where a single early mistake can alter the subsequent state distribution and derail the whole trajectory. Existing recipes…
Exploration-Driven Optimization for Test-Time Large Language Model Reasoning
Changhao Li, Yuchen Zhuang, Chenxiao Gao +4
Post-training techniques combined with inference-time scaling significantly enhance the reasoning and alignment capabilities of large language models (LLMs). However, a fundamental…
FlowRL: A Taxonomy and Modular Framework for Reinforcement Learning with Diffusion Policies
Chenxiao Gao, Edward Chen, Tianyi Chen +1
Thanks to their remarkable flexibility, diffusion models and flow models have emerged as promising candidates for policy representation. However, efficient reinforcement learning (…
GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning
Haitong Ma, Chenxiao Gao, Tianyi Chen +2
A commonly used family of RL algorithms for diffusion policies conducts softmax reweighting over samples from the behavior policy, which often induces an overgreedy policy and fail…
Reward Models in Deep Reinforcement Learning: A Survey
Rui Yu, Shenghua Wan, Yucen Wang +4
In reinforcement learning (RL), agents continually interact with the environment and use the feedback to refine their behavior. To guide policy optimization, reward models are intr…
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
Chengxing Jia, Fuxiang Zhang, Yi-Chen Li +5
Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identificatio…
Reinforced In-Context Black-Box Optimization
Lei Song, Chenxiao Gao, Ke Xue +5
Black-Box Optimization (BBO) has found successful applications in many fields of science and engineering. Recently, there has been a growing interest in meta-learning particular co…
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations
Renzhe Zhou, Chen-Xiao Gao, Zongzhang Zhang +1
Generalization and sample efficiency have been long-standing issues concerning reinforcement learning, and thus the field of Offline Meta-Reinforcement Learning~(OMRL) has gained i…
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
Chen-Xiao Gao, Chenyang Wu, Mingjun Cao +3
Decision Transformer (DT), which employs expressive sequence modeling techniques to perform action generation, has emerged as a promising approach to offline policy optimization. H…
Model Generation with Provable Coverability for Offline Reinforcement Learning
Chengxing Jia, Hao Yin, Chenxiao Gao +4
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could ad…