1 citations · 2 across the 13 of their papers we have counts for
16 papers
Selection-Aware Stress Testing for Interactive Agents
Yang Xu, Chenang Li, Jiefu Zhang +3
Agent evaluations often use one benchmark to choose a workflow and then search for task types where its advantage weakens, so both conclusions are selected from the same data. We i…
HyperSim: A Holistic Sim-To-Real Framework For Robust Robotic Manipulation
Junyi Dong, Haotian Luo, Ziwei Xu +11
Scaling data volume and diversity is critical for generalizing embodied intelligence. While synthetic data generation offers a scalable alternative to expensive physical data acqui…
Core-Halo Decomposition: Decentralizing Large-Scale Fixed-Point Problems
Haixiang, Yang Xu, Jiefu Zhang +4
We study solving large-scale fixed-point equation \(x^\star=\bar F(x^\star)\) with decomposition. Standard strict decomposition assigns each agent a disjoint block and evaluates up…
Online Bayesian Calibration under Gradual and Abrupt System Changes
Yang Xu, Chiwoo Park
Bayesian model calibration is central to digital twins and computer experiments, as it aligns model outputs with field observations by estimating calibration parameters and correct…
Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking
Yang Xu, Jiefu Zhang, Haixiang Sun +3
Adaptive prompt and program search makes LLM evaluation selection-sensitive. Once benchmark items are reused inside tuning, the observed winner's score need not estimate the fresh-…
AGPO: Asymmetric Group Policy Optimization for Verifiable Reasoning and Search Ads Relevance at JD
Yang Xu, Kun Yao, Yiming Deng +3
Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated notable success in enhancing the reasoning performance of large language models (LLMs). However, recent studi…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
SAT: Sequential Agent Tuning for Coordinator Free Plug and Play Multi-LLM Training with Monotonic Improvement Guarantees
Yi Xie, Yangyang Xu, Yi Fan +1
Large language models (LLMs) with a large number of parameters achieve strong performance but are often prohibitively expensive to deploy. Recent work explores using teams of small…
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
Jiefu Zhang, Yang Xu, Vaneet Aggarwal
Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-…
Persistent-Transient Policy Evaluation for Markov Chains via Minimal Peripheral Quotients
Yang Xu, Vaneet Aggarwal
We study fixed-policy evaluation for finite Markov chains that may be reducible and periodic. Classical evaluation methods with gain and bias decomposition are not always diagnosti…
Efficient Q-Learning and Actor-Critic Methods for Robust Average-Reward Reinforcement Learning
Yang Xu, Swetha Ganesh, Vaneet Aggarwal
We study model-free methods for distributionally robust infinite-horizon average-reward Markov decision processes (MDPs). We present non-asymptotic convergence analyses of Q-learni…
Global Convergence for Average Reward Constrained MDPs with Primal-Dual Actor Critic Algorithm
Yang Xu, Swetha Ganesh, Washim Uddin Mondal +2
This paper investigates infinite-horizon average reward Constrained Markov Decision Processes (CMDPs) with general parametrization. We propose a Primal-Dual Natural Actor-Critic al…
Finite-Sample Analysis of Policy Evaluation for Robust Average Reward Reinforcement Learning
Yang Xu, Washim Uddin Mondal, Vaneet Aggarwal
We present the first finite-sample analysis of policy evaluation in robust average-reward Markov Decision Processes (MDPs). Prior work in this setting have established only asympto…
Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach
Yang Xu, Vaneet Aggarwal
We address the problem of quantum reinforcement learning (QRL) under model-free settings with quantum oracle access to the Markov Decision Process (MDP). This paper introduces a Qu…
Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions
Jiayu Chen, Bhargav Ganguly, Yang Xu +3
Deep generative models (DGMs) have demonstrated great success across various domains, particularly in generating texts, images, and videos using models trained from offline data. S…
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes
Bhargav Ganguly, Yang Xu, Vaneet Aggarwal
This paper investigates the potential of quantum acceleration in addressing infinite horizon Markov Decision Processes (MDPs) to enhance average reward outcomes. We introduce an in…