6 papers · 1 filter
Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning
Chihyeon Song, Jaewoo Lee, Jinkyoo Park
Offline-to-Online Reinforcement Learning (O2O RL) faces a critical dilemma in balancing the use of a fixed offline dataset with newly collected online experiences. Standard methods…
Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function
Hyeongyu Kang, Jaewoo Lee, Woocheol Shin +2
Diffusion models excel at generating high-likelihood samples but often require alignment with downstream objectives. Existing fine-tuning methods for diffusion models significantly…
PRInTS: Reward Modeling for Long-Horizon Information Seeking
Jaewoo Lee, Archiki Prasad, Justin Chih-Yao Chen +3
Information-seeking is a core capability for AI agents, requiring them to gather and reason over tool-generated information across long trajectories. However, such multi-step infor…
Diffusion Alignment as Variational Expectation-Maximization
Jaewoo Lee, Minsu Kim, Sanghyeok Choi +7
Diffusion alignment aims to optimize diffusion models for the downstream objective. While existing methods based on reinforcement learning or direct backpropagation achieve conside…
TAMP: Token-Adaptive Layerwise Pruning in Multimodal Large Language Models
Jaewoo Lee, Keyang Xuan, Chanakya Ekbote +3
Multimodal Large Language Models (MLLMs) have shown remarkable versatility in understanding diverse multimodal data and tasks. However, these capabilities come with an increased mo…
Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization
Taeyoung Yun, Kiyoung Om, Jaewoo Lee +2
Optimizing high-dimensional and complex black-box functions is crucial in numerous scientific applications. While Bayesian optimization (BO) is a powerful method for sample-efficie…