1 citations · 1 across the 7 of their papers we have counts for
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Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs
Seohyun Lee, Dong-Jun Han, Seyyedali Hosseinalipour +1
Feedback-based on-policy self-distillation has emerged as a promising approach for enabling foundation models, more specifically Large Language Models (LLMs), to learn from their o…
Reason in the Words You Speak: Idiolectal Paraphrasing Off-Policy Traces for Reasoning Distillation in VideoLLMs
Ji Soo Lee, Jinyoung Park, Seohyun Lee +4
Recent large language models achieve strong performance on complex reasoning tasks, where reinforcement learning with Group Relative Policy Optimization (GRPO) has emerged as a lea…
VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement
Seohyun Lee, Seoung Choi, Dohwan Ko +2
As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video…
Detecting and Mitigating Backdoor Attacks in OTA-FL Systems: A Two-Stage Robust Aggregation Scheme
Xiaoyan Ma, Seohyun Lee, Taejoon Kim +1
Over-the-air federated learning (OTA-FL) improves communication efficiency by exploiting the superposition property of wireless channels, but this same property also creates a crit…
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning
Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2
Recent works have advanced feedback-based learning systems, whereby a foundation model is able to intake incoming feedback (e.g., from a user) to self-improve, creating a self-loop…
MoE-GRPO: Optimizing Mixture-of-Experts via Reinforcement Learning in Vision-Language Models
Dohwan Ko, Jinyoung Park, Seoung Choi +3
Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for ea…