9 papers
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…
Aligning Few-Step Generative Models by Amortizing Sample-based Variational Inference
Jaewoo Lee, Hyeongyu Kang, Dohyun Kim +9
Aligning a few-step generative model is challenging, since existing alignment frameworks typically rely on restrictive assumptions: a tractable likelihood, a specific ODE/SDE solve…
Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
Taeyoung Yun, Woocheol Shin, Inhyuck Song +2
Gaussian Process (GP) kernels are central to Bayesian optimization (BO), yet designing effective kernels for high-dimensional problems still relies on extensive manual engineering.…
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…
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…