Author

Seohyun Lee

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#machine learning May 2026

Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback

This work introduces SPEAR (Self-Play Enhancement via Advantage-Weighted Refinement), an efficient online learning algorithm for federated LLM fine-tuning that requires only an interaction-level binary satisfaction signal together with non-answer feedback, without requiring a ground-truth answer to be provided, and doe...

Seohyun Lee, Wen-Zhi Fang, Dong-Jun Han et al. · arXiv.org · 1 citation

#machine learning Preprint Sep 2026

Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs

Fire (Fisher-Informed REcalibration), a dual-branch framework that recalibrates the supervision applied to correct and incorrect on-policy outputs during fine-tuning, is proposed, which provides substantially more stable self-distillation while maintaining strong downstream performance, particularly in settings where s...

Seohyun Lee, Dong-Jun Han, Seyyedali Hosseinalipour et al. · 0 citations

Preprint Aug 2026

Reason in the Words You Speak: Idiolectal Paraphrasing Off-Policy Traces for Reasoning Distillation in VideoLLMs

Echo-GRPO is proposed, a framework that lets the model reason in the words it speaks, and idiolectal paraphrasing is a plug-in module that consistently improves both RL and supervised fine-tuning frameworks for reasoning distillation, demonstrating that policy-aligned supervision extends beyond GRPO.

Ji Soo Lee, Jinyoung Park, Seohyun Lee et al. · 0 citations