3 papers
cs.LG2026
When Does Online Imitation Learning Help in LLM Post-Training? The Role of (Non-)Realizability Beyond Horizon
Huaqing Zhang, Jingchu Gai, Juno Kim +2
Online imitation learning (IL), particularly on-policy distillation, has emerged as a strong LLM post-training approach, often outperforming offline supervised fine-tuning (SFT). Y…
cs.LG2026
Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory
Juno Kim, Eshaan Nichani, Denny Wu +2
Spectral optimizers such as Muon have recently shown strong empirical performance in large-scale language model training, but the source and extent of their advantage remain poorly…
cs.LG2026
Coverage Improvement and Fast Convergence of On-policy Preference Learning
Juno Kim, Jihun Yun, Jason D. Lee +1
Online on-policy preference learning algorithms for language model alignment such as online direct policy optimization (DPO) can significantly outperform their offline counterparts…