11 papers · 1 filter
TAFFY: A Task-Adaptive Tabular Foundation Model with In-Context Diversity
Zijian Li, Xiangchen Song, Gongxu Luo +7
Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with overall performance largely…
Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
Fan Feng, Yujia Zheng, Minghao Fu +5
Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approaches often rely on high-dime…
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
Yuejiang Liu, Fan Feng, Lingjing Kong +6
General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness remains challenging. Unlike policy learn…
Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
Fan Feng, Selena Ge, Minghao Fu +6
Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these approaches often overlook latent f…
From Generalist to Specialist Representation
Yujia Zheng, Fan Feng, Yuke Li +3
Given a generalist model, learning a task-relevant specialist representation is fundamental for downstream applications. Identifiability, the asymptotic guarantee of recovering the…
Factored Causal Representation Learning for Robust Reward Modeling in RLHF
Yupei Yang, Lin Yang, Wanxi Deng +5
A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models a…
FRoD: Full-Rank Efficient Fine-Tuning with Rotational Degrees for Fast Convergence
Guoan Wan, Tianyu Chen, Fangzheng Feng +2
Parameter-efficient fine-tuning (PEFT) methods have emerged as a practical solution for adapting large foundation models to downstream tasks, reducing computational and memory cost…
Learning Interactive World Model for Object-Centric Reinforcement Learning
Fan Feng, Phillip Lippe, Sara Magliacane
Agents that understand objects and their interactions can learn policies that are more robust and transferable. However, most object-centric RL methods factor state by individual o…
Online Time Series Forecasting with Theoretical Guarantees
Zijian Li, Changze Zhou, Minghao Fu +6
This paper is concerned with online time series forecasting, where unknown distribution shifts occur over time, i.e., latent variables influence the mapping from historical to futu…
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
Caleb Chuck, Fan Feng, Carl Qi +4
Hindsight relabeling is a powerful tool for overcoming sparsity in goal-conditioned reinforcement learning (GCRL), especially in certain domains such as navigation and locomotion.…
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
Yupei Yang, Biwei Huang, Fan Feng +3
General intelligence requires quick adaption across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distr…