53 citations · 58 across the 14 of their papers we have counts for
19 papers
Demystifying On-Policy Distillation: Where Does the Teacher's Advantage Live?
Yiping Li, Fan Zhao, Zhibo Hou +2
On-policy distillation (OPD) is increasingly studied as a cheaper alternative to reinforcement learning, yet it remains a form of knowledge distillation (KD) that relies on teacher…
Vision: "What Don't I Know About User's Preferences?" How Uncertainty Estimation Improves Proactive Assistant Alignment
Zhiyu An, Ziyi Xuan, Yu Yang +1
Proactive Smart Assistants (PSAs) increasingly use general knowledge, learned patterns from group-level data, patterns from individual user's data, and recent sensor observations t…
Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry
Yiping Li, Zhiyu An, Wan Du
A language model's ability to solve a task individually does not ensure that a team can solve it under unequal access to information and communication. SANZARU examines this gap th…
Neither True nor False: Do LLMs Represent Truth-Value Gaps in Plural Predication?
Duaa Nakshbandi, Zhiyu An, Yiping Li +2
Large Language Models (LLMs) are increasingly being used to summarize and verify claims in critical sources that inform consequential decisions. Such claims are full of plural defi…
Sparse-TD-JEPA: Improving Zero-Shot Reinforcement Learning With Sparse Latent Task Representations
Duaa Nakshbandi, Zhiyu An, Wan Du
Successor-feature-based zero-shot reinforcement learning methods pre-train a task encoder and successor-feature predictor on offline, reward-free data, then recover a policy at tes…
From Memorization to Absorption: Mixed-Policy RL for Continual Knowledge Injection
Zhibo Hou, Fan Zhao, Zhiyu An +1
Continual knowledge injection is essential for keeping large language models up-to-date in a fast-evolving world. Existing methods rely on supervised fine-tuning (SFT), which memor…
When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration
Yiping Li, Zhiyu An, Wan Du
Multi-agent LLM systems are moving beyond discrete-token messages toward richer relays that preserve internal state. Recent work such as LatentMAS transmits full key-value (KV) cac…
Open Problems in Differentiable Social Choice: Learning Mechanisms, Decisions, and Alignment
Zhiyu An, Wan Du
Social choice has become a foundational component of modern machine learning systems. From auctions and resource allocation to the alignment of large generative models, machine lea…
Differential Voting: Loss Functions For Axiomatically Diverse Aggregation of Heterogeneous Preferences
Zhiyu An, Duaa Nakshbandi, Wan Du
Reinforcement learning from human feedback (RLHF) implicitly aggregates heterogeneous human preferences into a single utility function, even though the underlying utilities of the…
DIML: Differentiable Inverse Mechanism Learning from Behaviors of Multi-Agent Learning Trajectories
Zhiyu An, Wan Du
We study inverse mechanism learning: recovering an unknown incentive-generating mechanism from observed strategic interaction traces of self-interested learning agents. Unlike inve…
Representational Homomorphism Predicts and Improves Compositional Generalization In Transformer Language Model
Zhiyu An, Wan Du
Compositional generalization-the ability to interpret novel combinations of familiar components-remains a persistent challenge for neural networks. Behavioral evaluations reveal \e…
MoralReason: Generalizable Moral Decision Alignment For LLM Agents Using Reasoning-Level Reinforcement Learning
Zhiyu An, Wan Du
Large language models are increasingly influencing human moral decisions, yet current approaches focus primarily on evaluating rather than actively steering their moral decisions.…
Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring
Zhibo Hou, Zhiyu An, Wan Du
When there exists an unlearnable source of randomness (noisy-TV) in the environment, a naively intrinsic reward driven exploring agent gets stuck at that source of randomness and f…
Disentangling Uncertainties by Learning Compressed Data Representation
Zhiyu An, Zhibo Hou, Wan Du
We study aleatoric and epistemic uncertainty estimation in a learned regressive system dynamics model. Disentangling aleatoric uncertainty (the inherent randomness of the system) f…
Golden-Retriever: High-Fidelity Agentic Retrieval Augmented Generation for Industrial Knowledge Base
Zhiyu An, Xianzhong Ding, Yen-Chun Fu +3
This paper introduces Golden-Retriever, designed to efficiently navigate vast industrial knowledge bases, overcoming challenges in traditional LLM fine-tuning and RAG frameworks wi…
A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control
Xianzhong Ding, Zhiyu An, Arya Rathee +1
Model-Based Reinforcement Learning (MBRL) has been widely studied for Heating, Ventilation, and Air Conditioning (HVAC) control in buildings. One of the critical challenges is the…
Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
Zhiyu An, Xianzhong Ding, Wan Du
Recent research has shown the potential of Model-based Reinforcement Learning (MBRL) to enhance energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems. Howe…
Reward Bound for Behavioral Guarantee of Model-based Planning Agents
Zhiyu An, Xianzhong Ding, Wan Du
Recent years have seen an emerging interest in the trustworthiness of machine learning-based agents in the wild, especially in robotics, to provide safety assurance for the industr…
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Xianzhong Ding, Le Chen, Murali Emani +6
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their perform…