#self‑distillation
11 papers · 1 filter
-OPSD: Deriving with Policy Optimization, Training with Self-Distillation
Jiawei Xu, Minghui Liu, Juzheng Zhang +2
The paper proposes β‑OPSD, a generalized on‑policy self‑distillation method that treats the KL regularization weight as a tunable parameter, enabling a controlled interpolation bet…
Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory
Hanzuo Liu, Xuan Qi, Chunyu Liu +6
The paper proposes CoMem, a method that stores intermediate transformer layer states as memory to enable efficient long‑context retrieval, showing that using lower‑mid layers for c…
Group-Reflective Self-Distillation for Agentic Reinforcement Learning
Binbin Zheng, Zijun Xie, Guanqun Zhao +4
The paper introduces Group-Reflective Self-Distillation (GRSD), a method that uses a policy's own verified rollouts to generate privileged guidance for better credit assignment in…
Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
Xingjian Wu, Junlin Liu, Xingchen Liu +6
The paper introduces Contrastive Reinforced Policy Optimization (CRPO), a method that frames on‑policy self‑distillation for large language models as a contrastive learning problem…
From Scoring to Acting: Outcome-Verified Comparative Self-Distillation for LLM Agents
Xu Xia, Jinghua Piao, Min Yang +3
The paper introduces Outcome-Verified Comparative Self-Distillation (OVCSD), a method that lets large language model agents internalize skills by supervising them with teachers who…
Learning from the Future: Privileged Self-Distillation for Sequential Recommendation
Jiakai Tang, Yang Zhang, See-Kiong Ng +4
The paper introduces Privileged Self-Distillation (PSD), a method that uses future user interactions as training‑only privileged information to improve sequential recommendation mo…