5 papers
On-Policy Delta Distillation for Multilingual Math Reasoning
Byeongho Heo, Jaehui Hwang, Sangdoo Yun +1
On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingual settings remains underexp…
On-Policy Delta Distillation
Byeongho Heo, Jaehui Hwang, Sangdoo Yun +1
The paper proposes On-Policy Delta Distillation (OPD²), a new on‑policy distillation method that uses a delta signal—the difference between a teacher LLM and its pre‑tuned base mod…
Oops, Wait: Discourse Tokens Matter in Reasoning Model
Jaehui Hwang, Byeongho Heo, Sangdoo Yun +1
Recent studies suggest that even data-efficient training with (1K) reasoning trajectories can induce non-trivial reasoning capabilities in large language models through pos…
MuCo: Multi-turn Contrastive Learning for Multimodal Embedding Model
Geonmo Gu, Byeongho Heo, Jaemyung Yu +7
Universal Multimodal embedding models built on Multimodal Large Language Models (MLLMs) have traditionally employed contrastive learning, which aligns representations of query-targ…
What Defines Good Reasoning in LLMs? Dissecting Reasoning Steps with Multi-Aspect Evaluation
Heejin Do, Jaehui Hwang, Dongyoon Han +2
Evaluating large language models (LLMs) on final-answer correctness is the dominant paradigm. This approach, however, provides a coarse signal for model improvement and overlooks t…