3 papers
cs.LG2026
ACE: Adapter Consolidation across Experts for Parameter-Efficient Fine-Tuning of MoE LLMs
Ahin Lee, Sehyun Yun, Joonha Park +1
Parameter-efficient fine-tuning (PEFT) of mixture-of-experts (MoE) models commonly attaches a separate low-rank adapter to each expert. This expert-wise design fragments adaptation…
cs.RO2026
Premover: Fast Vision-Language-Action Control via Early Execution During Instruction Delivery
Joonha Park, Jiseung Jeong, Taesik Gong
Vision-Language-Action (VLA) policies are typically evaluated under the assumption that the robot starts acting only after the user has finished typing or speaking. In real interac…
stat.CO2026
Modular Markov chain Monte Carlo with application to multimodal sampling
Joonha Park
We develop a modular approach to Markov chain Monte Carlo (MCMC) sampling for unnormalized target densities. In this approach, Markov chains are constructed in parallel, each const…