Saved in:
| Main Authors: | Li, Yuan, Liu, Zhengzhong, Xing, Eric |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11953 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
by: Kim, Gyuhak, et al.
Published: (2025)
by: Kim, Gyuhak, et al.
Published: (2025)
Preserving Diversity in Supervised Fine-Tuning of Large Language Models
by: Li, Ziniu, et al.
Published: (2024)
by: Li, Ziniu, et al.
Published: (2024)
How Does Controllability Emerge In Language Models During Pretraining?
by: She, Jianshu, et al.
Published: (2025)
by: She, Jianshu, et al.
Published: (2025)
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
by: Dong, Guanting, et al.
Published: (2023)
by: Dong, Guanting, et al.
Published: (2023)
The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models
by: Riemer, Matthew, et al.
Published: (2025)
by: Riemer, Matthew, et al.
Published: (2025)
Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
by: Ding, Fei, et al.
Published: (2025)
by: Ding, Fei, et al.
Published: (2025)
LIA: Supervised Fine-Tuning of Large Language Models for Automatic Issue Assignment
by: Khosravani, Arsham, et al.
Published: (2026)
by: Khosravani, Arsham, et al.
Published: (2026)
Safety-Aware Fine-Tuning of Large Language Models
by: Choi, Hyeong Kyu, et al.
Published: (2024)
by: Choi, Hyeong Kyu, et al.
Published: (2024)
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)
RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
by: Wei, Quan, et al.
Published: (2025)
by: Wei, Quan, et al.
Published: (2025)
Data Efficient Adaptation in Large Language Models via Continuous Low-Rank Fine-Tuning
by: Han, Xiao, et al.
Published: (2025)
by: Han, Xiao, et al.
Published: (2025)
Large Language Models Reasoning Abilities Under Non-Ideal Conditions After RL-Fine-Tuning
by: Tian, Chang, et al.
Published: (2025)
by: Tian, Chang, et al.
Published: (2025)
Memorization in Fine-Tuned Large Language Models
by: Savine, Danil
Published: (2025)
by: Savine, Danil
Published: (2025)
On-Policy Supervised Fine-Tuning for Efficient Reasoning
by: Zhao, Anhao, et al.
Published: (2026)
by: Zhao, Anhao, et al.
Published: (2026)
Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
by: Zhang, Minmin, et al.
Published: (2026)
by: Zhang, Minmin, et al.
Published: (2026)
MixLoRA: Enhancing Large Language Models Fine-Tuning with LoRA-based Mixture of Experts
by: Li, Dengchun, et al.
Published: (2024)
by: Li, Dengchun, et al.
Published: (2024)
Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)
by: Zhu, Wenhong, et al.
Published: (2025)
Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models
by: Ramakrishnan, Badrinath, et al.
Published: (2025)
by: Ramakrishnan, Badrinath, et al.
Published: (2025)
Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models
by: Shen, Minghe, et al.
Published: (2025)
by: Shen, Minghe, et al.
Published: (2025)
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
by: Zheng, Wenhao, et al.
Published: (2025)
by: Zheng, Wenhao, 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)
Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models
by: Wang, Yulong, et al.
Published: (2024)
by: Wang, Yulong, et al.
Published: (2024)
PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
by: Liu, Yi, et al.
Published: (2025)
by: Liu, Yi, et al.
Published: (2025)
A Self-Supervised Reinforcement Learning Approach for Fine-Tuning Large Language Models Using Cross-Attention Signals
by: Kiruluta, Andrew, et al.
Published: (2025)
by: Kiruluta, Andrew, et al.
Published: (2025)
Surgery: Mitigating Harmful Fine-Tuning for Large Language Models via Attention Sink
by: Liu, Guozhi, et al.
Published: (2026)
by: Liu, Guozhi, et al.
Published: (2026)
Rotation-Preserving Supervised Fine-Tuning
by: Jin, Hangzhan, et al.
Published: (2026)
by: Jin, Hangzhan, et al.
Published: (2026)
On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
by: Wang, Shumin, et al.
Published: (2026)
by: Wang, Shumin, et al.
Published: (2026)
Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization
by: Jern, Linus, et al.
Published: (2025)
by: Jern, Linus, et al.
Published: (2025)
Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization
by: Savage, Thomas, et al.
Published: (2024)
by: Savage, Thomas, et al.
Published: (2024)
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models
by: Ding, Hongcheng, et al.
Published: (2024)
by: Ding, Hongcheng, et al.
Published: (2024)
Data Provenance Auditing of Fine-Tuned Large Language Models with a Text-Preserving Technique
by: Li, Yanming, et al.
Published: (2025)
by: Li, Yanming, et al.
Published: (2025)
The Thinking Therapist: Training Large Language Models to Deliver Acceptance and Commitment Therapy using Supervised Fine-Tuning and Odds Ratio Policy Optimization
by: Tahir, Talha
Published: (2025)
by: Tahir, Talha
Published: (2025)
DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization
by: Li, Gang, et al.
Published: (2025)
by: Li, Gang, et al.
Published: (2025)
Phased Instruction Fine-Tuning for Large Language Models
by: Pang, Wei, et al.
Published: (2024)
by: Pang, Wei, et al.
Published: (2024)
Linearization Explains Fine-Tuning in Large Language Models
by: Afzal, Zahra Rahimi, et al.
Published: (2026)
by: Afzal, Zahra Rahimi, et al.
Published: (2026)
Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
by: Jiang, Haitao, et al.
Published: (2026)
by: Jiang, Haitao, et al.
Published: (2026)
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches
by: Dong, Yanjie, et al.
Published: (2024)
by: Dong, Yanjie, et al.
Published: (2024)
Artificial Entanglement in the Fine-Tuning of Large Language Models
by: Chen, Min, et al.
Published: (2026)
by: Chen, Min, et al.
Published: (2026)
Hyperbolic Fine-Tuning for Large Language Models
by: Yang, Menglin, et al.
Published: (2024)
by: Yang, Menglin, et al.
Published: (2024)
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
by: Zhang, Xinlu, et al.
Published: (2024)
by: Zhang, Xinlu, et al.
Published: (2024)
Similar Items
-
SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
by: Kim, Gyuhak, et al.
Published: (2025) -
Preserving Diversity in Supervised Fine-Tuning of Large Language Models
by: Li, Ziniu, et al.
Published: (2024) -
How Does Controllability Emerge In Language Models During Pretraining?
by: She, Jianshu, et al.
Published: (2025) -
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
by: Dong, Guanting, et al.
Published: (2023) -
The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models
by: Riemer, Matthew, et al.
Published: (2025)