6 papers · 1 filter
WASD: Wasserstein-based Knowledge Distillation for Large Language Models
Byeonghu Na, Donghyeok Shin, Yeongmin Kim +2
Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and memory costs at inference time.…
Best-of- Guidance for Test-time Diffusion Alignment
Richard Lee Kim, Yeongmin Kim, Gyuwon Sim +3
Diffusion models achieve strong generative performance but often struggle to align generated samples with human preferences measured by a reward model. A simple yet effective algor…
Multi-Agent Debate and Visual Information Extraction for SeePhys Pro: A 1st-Place Technical Report from ICML 2026 AI4Math Track 3 Challenge
Jiseok Kwak, Suhyeon Jo, Taewoo Kim +3
This technical report presents our approach to Challenge Track~3: SeePhys Pro at the 3rd AI for Math Workshop, where the task is to answer college-level physics questions whose sta…
Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
Gyuwon Sim, Sumin Lee, Heesun Bae +5
Missing data frequently arises across diverse domains, including time-series and image domains. In the real world, missing occurrences often depend on the unobservable values thems…
Semantic-aware Wasserstein Policy Regularization for Large Language Model Alignment
Byeonghu Na, Hyungho Na, Yeongmin Kim +4
Large language models (LLMs) are commonly aligned with human preferences using reinforcement learning from human feedback (RLHF). In this method, LLM policies are generally optimiz…
Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
Yeongmin Kim, Donghyeok Shin, Byeonghu Na +3
Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies an efficient test-time…