11 papers
Token-level Response-visual Attention Guidance for Multimodal LLMs Knowledge Distillation
Jaehyun Jang, Eunseop Yoon, Hee Suk Yoon +3
While knowledge distillation (KD) is widely adopted for training lightweight models by leveraging supervision from larger teacher models, relying solely on output token distributio…
Transcript-Free Flow-Matching Text-to-Speech via Speech Feature Conditioning
SooHwan Eom, Hee Suk Yoon, Eunseop Yoon +2
Recent flow-matching text-to-speech (TTS) models, such as F5-TTS, rely on a reference transcript at inference time, obtained from an external ASR system. This dependency makes zero…
Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding
Hee Suk Yoon, Eunseop Yoon, Jaehyun Jang +6
While on-policy distillation offers dense supervision for training small reasoning models, its optimization dynamics in the multimodal domain remain under-explored. In this work, w…
PDCR: Perception-Decomposed Confidence Reward for Vision-Language Reasoning
Hee Suk Yoon, Eunseop Yoon, Ji Woo Hong +6
Reinforcement Learning with Verifiable Rewards (RLVR) traditionally relies on a sparse, outcome-based signal. Recent work shows that providing a fine-grained, model-intrinsic signa…
High-Fidelity Text-to-Image Generation from Pre-Trained Vision-Language Models via Distribution-Conditioned Diffusion Decoding
Ji Woo Hong, Hee Suk Yoon, Gwanhyeong Koo +5
Recent large-scale vision-language models (VLMs) have shown remarkable text-to-image generation capabilities, yet their visual fidelity remains constrained by the discrete image to…
PACR: Progressively Ascending Confidence Reward for LLM Reasoning
Eunseop Yoon, Hee Suk Yoon, Jaehyun Jang +5
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly improved LLM reasoning, but its sparse, outcome-based reward provides no guidance for intermediate steps, sl…