Curriculum Learning-Guided Progressive Distillation in Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Cao, Jincheng, Zeng, Fanzhi, Liu, Leqi, Mokhtari, Aryan |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
by: Li, Shuozhe, et al.
Published: (2026)
by: Li, Shuozhe, et al.
Published: (2026)
Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models
by: Bello, Femi, et al.
Published: (2025)
by: Bello, Femi, et al.
Published: (2025)
Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning Framework
by: Liu, Lingyuan, et al.
Published: (2025)
by: Liu, Lingyuan, et al.
Published: (2025)
Collaborative Adaptive Curriculum for Progressive Knowledge Distillation
by: Liu, Jing, et al.
Published: (2026)
by: Liu, Jing, et al.
Published: (2026)
Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)
by: Block, Jacob L., et al.
Published: (2025)
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
by: Liu, Chen, et al.
Published: (2024)
by: Liu, Chen, et al.
Published: (2024)
GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
by: Tang, Xiaohang, et al.
Published: (2026)
by: Tang, Xiaohang, et al.
Published: (2026)
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
by: Collins, Liam, et al.
Published: (2024)
by: Collins, Liam, et al.
Published: (2024)
Prompting Fairness: Integrating Causality to Debias Large Language Models
by: Li, Jingling, et al.
Published: (2024)
by: Li, Jingling, et al.
Published: (2024)
Delta Knowledge Distillation for Large Language Models
by: Cao, Yihan, et al.
Published: (2025)
by: Cao, Yihan, et al.
Published: (2025)
Large Language Models Explore by Latent Distilling
by: Zeng, Yuanhao, et al.
Published: (2026)
by: Zeng, Yuanhao, et al.
Published: (2026)
A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement
by: Yuan, Hui, et al.
Published: (2024)
by: Yuan, Hui, et al.
Published: (2024)
Internalizing Meta-Experience into Memory for Guided Reinforcement Learning in Large Language Models
by: Huang, Shiting, et al.
Published: (2026)
by: Huang, Shiting, et al.
Published: (2026)
Personalized Language Modeling from Personalized Human Feedback
by: Li, Xinyu, et al.
Published: (2024)
by: Li, Xinyu, et al.
Published: (2024)
Curriculum Guided Personalized Subgraph Federated Learning
by: Kang, Minku, et al.
Published: (2025)
by: Kang, Minku, et al.
Published: (2025)
QOG:Question and Options Generation based on Language Model
by: Zhou, Jincheng
Published: (2024)
by: Zhou, Jincheng
Published: (2024)
Large Language Models aren't all that you need
by: Holla, Kiran Voderhobli, et al.
Published: (2024)
by: Holla, Kiran Voderhobli, et al.
Published: (2024)
Learning Robust Reasoning through Guided Adversarial Self-Play
by: Li, Shuozhe, et al.
Published: (2026)
by: Li, Shuozhe, et al.
Published: (2026)
Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPO
by: Yu, Bowen, et al.
Published: (2026)
by: Yu, Bowen, et al.
Published: (2026)
Rethinking the Role of Temperature in Large Language Model Distillation
by: Luong, Hoang-Chau, et al.
Published: (2026)
by: Luong, Hoang-Chau, et al.
Published: (2026)
Sculpting Latent Spaces With MMD: Disentanglement With Programmable Priors
by: Fruytier, Quentin, et al.
Published: (2025)
by: Fruytier, Quentin, et al.
Published: (2025)
SPaCe: Unlocking Sample-Efficient Large Language Models Training With Self-Pace Curriculum Learning
by: Do, Dai, et al.
Published: (2025)
by: Do, Dai, et al.
Published: (2025)
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
by: Xi, Zhiheng, et al.
Published: (2024)
by: Xi, Zhiheng, et al.
Published: (2024)
Learning Progress Driven Multi-Agent Curriculum
by: Zhao, Wenshuai, et al.
Published: (2022)
by: Zhao, Wenshuai, et al.
Published: (2022)
Large Language Model Distilling Medication Recommendation Model
by: Liu, Qidong, et al.
Published: (2024)
by: Liu, Qidong, et al.
Published: (2024)
Distillation of Large Language Models via Concrete Score Matching
by: Kim, Yeongmin, et al.
Published: (2025)
by: Kim, Yeongmin, et al.
Published: (2025)
Eurekaverse: Environment Curriculum Generation via Large Language Models
by: Liang, William, et al.
Published: (2024)
by: Liang, William, et al.
Published: (2024)
Structured Agent Distillation for Large Language Model
by: Liu, Jun, et al.
Published: (2025)
by: Liu, Jun, et al.
Published: (2025)
Overcoming Uncertain Incompleteness for Robust Multimodal Sequential Diagnosis Prediction via Curriculum Data Erasing Guided Knowledge Distillation
by: Koo, Heejoon
Published: (2024)
by: Koo, Heejoon
Published: (2024)
ChessArena: A Chess Testbed for Evaluating Strategic Reasoning Capabilities of Large Language Models
by: Liu, Jincheng, et al.
Published: (2025)
by: Liu, Jincheng, et al.
Published: (2025)
On Predictability of Reinforcement Learning Dynamics for Large Language Models
by: Cai, Yuchen, et al.
Published: (2025)
by: Cai, Yuchen, et al.
Published: (2025)
Offline Model-Based Optimization via Policy-Guided Gradient Search
by: Chemingui, Yassine, et al.
Published: (2024)
by: Chemingui, Yassine, et al.
Published: (2024)
An Interpretable and Scalable Framework for Evaluating Large Language Models
by: Qu, Xinhao, et al.
Published: (2026)
by: Qu, Xinhao, et al.
Published: (2026)
Provable Training Data Identification for Large Language Models
by: Liu, Zhenlong, et al.
Published: (2025)
by: Liu, Zhenlong, et al.
Published: (2025)
LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation
by: Liu, Fangxin, et al.
Published: (2025)
by: Liu, Fangxin, et al.
Published: (2025)
Curriculum Learning for Small Code Language Models
by: Naïr, Marwa, et al.
Published: (2024)
by: Naïr, Marwa, et al.
Published: (2024)
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
by: Song, Siqing, et al.
Published: (2026)
by: Song, Siqing, et al.
Published: (2026)
Efficient Epistemic Uncertainty Estimation for Large Language Models via Knowledge Distillation
by: Park, Seonghyeon, et al.
Published: (2026)
by: Park, Seonghyeon, et al.
Published: (2026)
Diversity-Aware Reverse Kullback-Leibler Divergence for Large Language Model Distillation
by: Luong, Hoang-Chau, et al.
Published: (2026)
by: Luong, Hoang-Chau, et al.
Published: (2026)
Lillama: Large Language Models Compression via Low-Rank Feature Distillation
by: Sy, Yaya, et al.
Published: (2024)
by: Sy, Yaya, et al.
Published: (2024)
Similar Items
-
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
by: Li, Shuozhe, et al.
Published: (2026) -
Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models
by: Bello, Femi, et al.
Published: (2025) -
Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning Framework
by: Liu, Lingyuan, et al.
Published: (2025) -
Collaborative Adaptive Curriculum for Progressive Knowledge Distillation
by: Liu, Jing, et al.
Published: (2026) -
Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)