1 citations · 4 across the 17 of their papers we have counts for
10 papers · 1 filter
PBSD: Privileged Bayesian Self-Distillation for Long-Horizon Credit Assignment
Yang Tian, Rui Wang, Xumeng Wen +5
Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final correctness but provide lim…
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
Zizheng Zhang, Yiming Li, Justin Xu +6
In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasingly used to automate this process, act…
Holdout-Loss-Based Data Selection for LLM Finetuning via In-Context Learning
Ling Zhang, Xianliang Yang, Juwon Yu +4
Fine-tuning large pretrained language models is a common approach for aligning them with human preferences, but noisy or off-target examples can dilute supervision. While small, we…
In-Context Compositional Q-Learning for Offline Reinforcement Learning
Qiushui Xu, Yuhao Huang, Yushu Jiang +4
Accurate estimation of the Q-function is a central challenge in offline reinforcement learning. However, existing approaches often rely on a shared global Q-function, which is inad…
Sample-efficient LLM Optimization with Reset Replay
Zichuan Liu, Jinyu Wang, Lei Song +1
Recent advancements in LLM post-training, particularly through reinforcement learning and preference optimization, are key to boosting their reasoning capabilities. However, these…
Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Yiyuan Yang, Zichuan Liu, Lei Song +6
Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for trans…
Learning to Select In-Context Demonstration Preferred by Large Language Model
Zheng Zhang, Shaocheng Lan, Lei Song +3
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks during inference using only a few demonstrations. However, ICL performance is highly dependent…
C-MORL: Multi-Objective Reinforcement Learning through Efficient Discovery of Pareto Front
Ruohong Liu, Yuxin Pan, Linjie Xu +4
Multi-objective reinforcement learning (MORL) excels at handling rapidly changing preferences in tasks that involve multiple criteria, even for unseen preferences. However, previou…
Unveiling Markov Heads in Pretrained Language Models for Offline Reinforcement Learning
Wenhao Zhao, Qiushui Xu, Linjie Xu +4
Recently, incorporating knowledge from pretrained language models (PLMs) into decision transformers (DTs) has generated significant attention in offline reinforcement learning (RL)…
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
Linjie Xu, Zichuan Liu, Alexander Dockhorn +4
One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MA…