Saved in:
| Main Author: | Shen, Ming |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.06094 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
by: Pang, Jinlong, et al.
Published: (2025)
by: Pang, Jinlong, et al.
Published: (2025)
SED-SFT: Selectively Encouraging Diversity in Supervised Fine-Tuning
by: Chen, Yijie, et al.
Published: (2026)
by: Chen, Yijie, et al.
Published: (2026)
Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models
by: Liu, Ziche, et al.
Published: (2024)
by: Liu, Ziche, et al.
Published: (2024)
From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
by: Wu, Haodong, et al.
Published: (2026)
by: Wu, Haodong, et al.
Published: (2026)
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
by: Gupta, Sonam, et al.
Published: (2025)
by: Gupta, Sonam, et al.
Published: (2025)
Rethinking Supervised Fine-Tuning: Emphasizing Key Answer Tokens for Improved LLM Accuracy
by: Shi, Xiaofeng, et al.
Published: (2025)
by: Shi, Xiaofeng, et al.
Published: (2025)
Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning
by: Kopiczko, Dawid J., et al.
Published: (2026)
by: Kopiczko, Dawid J., et al.
Published: (2026)
Anchored Supervised Fine-Tuning
by: Zhu, He, et al.
Published: (2025)
by: Zhu, He, et al.
Published: (2025)
One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient
by: Ming, Rui, et al.
Published: (2025)
by: Ming, Rui, et al.
Published: (2025)
Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)
by: Zhu, Wenhong, et al.
Published: (2025)
Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation
by: Zhang, Lechen, et al.
Published: (2026)
by: Zhang, Lechen, et al.
Published: (2026)
Supervised In-Context Fine-Tuning for Generative Sequence Labeling
by: Dukić, David, et al.
Published: (2025)
by: Dukić, David, et al.
Published: (2025)
UFT: Unifying Supervised and Reinforcement Fine-Tuning
by: Liu, Mingyang, et al.
Published: (2025)
by: Liu, Mingyang, et al.
Published: (2025)
Mind the Gap: Data Rewriting for Stable Off-Policy Supervised Fine-Tuning
by: Zhao, Shiwan, et al.
Published: (2025)
by: Zhao, Shiwan, et al.
Published: (2025)
On the Role of Reasoning Patterns in the Generalization Discrepancy of Long Chain-of-Thought Supervised Fine-Tuning
by: Li, Zhaoyi, et al.
Published: (2026)
by: Li, Zhaoyi, et al.
Published: (2026)
Leveraging Web-Crawled Data for High-Quality Fine-Tuning
by: Zhou, Jing, et al.
Published: (2024)
by: Zhou, Jing, et al.
Published: (2024)
Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
by: Rallapalli, Swati, et al.
Published: (2025)
by: Rallapalli, Swati, et al.
Published: (2025)
Dynamic Jointly Batch Selection for Data Efficient Machine Translation Fine-Tuning
by: Ghanizadeh, Mohammad Amin, et al.
Published: (2025)
by: Ghanizadeh, Mohammad Amin, et al.
Published: (2025)
Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority
by: Shen, Zhanming, et al.
Published: (2026)
by: Shen, Zhanming, et al.
Published: (2026)
Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education
by: Vassar, Alexandra, et al.
Published: (2024)
by: Vassar, Alexandra, et al.
Published: (2024)
Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
by: Lu, Yuxiao, et al.
Published: (2024)
by: Lu, Yuxiao, et al.
Published: (2024)
Rethinking Weight Decay for Robust Fine-Tuning of Foundation Models
by: Tian, Junjiao, et al.
Published: (2024)
by: Tian, Junjiao, et al.
Published: (2024)
Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
by: Ding, Yi, et al.
Published: (2025)
by: Ding, Yi, et al.
Published: (2025)
Selection of LLM Fine-Tuning Data based on Orthogonal Rules
by: Li, Xiaomin, et al.
Published: (2024)
by: Li, Xiaomin, et al.
Published: (2024)
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
by: Li, Ming, et al.
Published: (2024)
by: Li, Ming, et al.
Published: (2024)
Supervised Fine-Tuning as Inverse Reinforcement Learning
by: Sun, Hao
Published: (2024)
by: Sun, Hao
Published: (2024)
Rethinking Reinforcement Fine-Tuning in LVLM: Convergence, Reward Decomposition, and Generalization
by: Adams, Carter, et al.
Published: (2026)
by: Adams, Carter, et al.
Published: (2026)
Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education
by: Ross, Emily, et al.
Published: (2025)
by: Ross, Emily, et al.
Published: (2025)
Towards Efficient Medical Reasoning with Minimal Fine-Tuning Data
by: Zhuang, Xinlin, et al.
Published: (2025)
by: Zhuang, Xinlin, et al.
Published: (2025)
TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
by: He, Xixiang, et al.
Published: (2025)
by: He, Xixiang, et al.
Published: (2025)
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
by: Deb, Rohan, et al.
Published: (2025)
by: Deb, Rohan, et al.
Published: (2025)
GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
by: Chen, Zihong, et al.
Published: (2025)
by: Chen, Zihong, et al.
Published: (2025)
Improving Translation Quality by Selecting Better Data for LLM Fine-Tuning: A Comparative Analysis
by: de Mello, Felipe Ribeiro Fujita, et al.
Published: (2025)
by: de Mello, Felipe Ribeiro Fujita, et al.
Published: (2025)
CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning
by: Ye, Yangfan, et al.
Published: (2025)
by: Ye, Yangfan, et al.
Published: (2025)
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
by: Wang, Fangxin, et al.
Published: (2026)
by: Wang, Fangxin, et al.
Published: (2026)
EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models
by: Tan, Zhiyu, et al.
Published: (2024)
by: Tan, Zhiyu, et al.
Published: (2024)
Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
by: Huang, Zeyu, et al.
Published: (2025)
by: Huang, Zeyu, et al.
Published: (2025)
Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning
by: Mecklenburg, Nick, et al.
Published: (2024)
by: Mecklenburg, Nick, et al.
Published: (2024)
Rethinking Parameter Sharing for LLM Fine-Tuning with Multiple LoRAs
by: Ban, Hao, et al.
Published: (2025)
by: Ban, Hao, et al.
Published: (2025)
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning
by: Wang, Shaobo, et al.
Published: (2025)
by: Wang, Shaobo, et al.
Published: (2025)
Similar Items
-
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
by: Pang, Jinlong, et al.
Published: (2025) -
SED-SFT: Selectively Encouraging Diversity in Supervised Fine-Tuning
by: Chen, Yijie, et al.
Published: (2026) -
Take the essence and discard the dross: A Rethinking on Data Selection for Fine-Tuning Large Language Models
by: Liu, Ziche, et al.
Published: (2024) -
From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
by: Wu, Haodong, et al.
Published: (2026) -
Selective Self-to-Supervised Fine-Tuning for Generalization in Large Language Models
by: Gupta, Sonam, et al.
Published: (2025)