Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
Fuente:
arXiv
Saved in:
| Main Authors: | Shi, Wenhang, Chen, Yiren, Bian, Shuqing, Zhao, Zhe, Dong, Jinhao, Hu, Pengfei, Lu, Wei, Du, Xiaoyong |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
by: Shi, Wenhang, et al.
Published: (2025)
by: Shi, Wenhang, et al.
Published: (2025)
Scaling Agentic Capabilities via Grounded Interaction Synthesis
by: Shi, Wenhang, et al.
Published: (2026)
by: Shi, Wenhang, et al.
Published: (2026)
Investigating the Impact of Rationales for LLMs on Natural Language Understanding
by: Shi, Wenhang, et al.
Published: (2025)
by: Shi, Wenhang, et al.
Published: (2025)
Joint Knowledge Editing for Information Enrichment and Probability Promotion
by: Shi, Wenhang, et al.
Published: (2024)
by: Shi, Wenhang, et al.
Published: (2024)
FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics
by: Du, Yupei, et al.
Published: (2023)
by: Du, Yupei, et al.
Published: (2023)
ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
by: Tang, Pengwei, et al.
Published: (2025)
by: Tang, Pengwei, et al.
Published: (2025)
Instruction Fine-Tuning: Does Prompt Loss Matter?
by: Huerta-Enochian, Mathew, et al.
Published: (2024)
by: Huerta-Enochian, Mathew, et al.
Published: (2024)
FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
by: Bian, Jieming, et al.
Published: (2025)
by: Bian, Jieming, et al.
Published: (2025)
Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs
by: Dong, Jiancheng, et al.
Published: (2024)
by: Dong, Jiancheng, et al.
Published: (2024)
Arch: An AI-Native Hardware Description Language for Register-Transfer Clocked Hardware Design
by: Zhao, Shuqing
Published: (2026)
by: Zhao, Shuqing
Published: (2026)
PAFT: Prompt-Agnostic Fine-Tuning
by: Wei, Chenxing, et al.
Published: (2025)
by: Wei, Chenxing, et al.
Published: (2025)
Refining Salience-Aware Sparse Fine-Tuning Strategies for Language Models
by: Liu, Xinxin, et al.
Published: (2024)
by: Liu, Xinxin, et al.
Published: (2024)
Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning
by: Shernazarov, Ulugbek, et al.
Published: (2026)
by: Shernazarov, Ulugbek, et al.
Published: (2026)
A Two-Phase Recall-and-Select Framework for Fast Model Selection
by: Cui, Jianwei, et al.
Published: (2024)
by: Cui, Jianwei, et al.
Published: (2024)
Effective Text Adaptation for LLM-based ASR through Soft Prompt Fine-Tuning
by: Ma, Yingyi, et al.
Published: (2024)
by: Ma, Yingyi, et al.
Published: (2024)
Optimizing Soft Prompt Tuning via Structural Evolution
by: Huang, Zhenzhen, et al.
Published: (2026)
by: Huang, Zhenzhen, et al.
Published: (2026)
Objective Matters: Fine-Tuning Objectives Shape Safety, Robustness, and Persona Drift
by: Vennemeyer, Daniel, et al.
Published: (2026)
by: Vennemeyer, Daniel, et al.
Published: (2026)
Prompt Tuning for Few-Shot Continual Learning Named Entity Recognition
by: Ren, Zhe
Published: (2025)
by: Ren, Zhe
Published: (2025)
State of the Art in Text Classification for South Slavic Languages: Fine-Tuning or Prompting?
by: Pungeršek, Taja Kuzman, et al.
Published: (2025)
by: Pungeršek, Taja Kuzman, 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)
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)
MindGYM: What Matters in Question Synthesis for Thinking-Centric Fine-Tuning?
by: Xu, Zhe, et al.
Published: (2025)
by: Xu, Zhe, et al.
Published: (2025)
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective
by: Peng, Dengyun, et al.
Published: (2025)
by: Peng, Dengyun, et al.
Published: (2025)
Data Diversity Matters for Robust Instruction Tuning
by: Bukharin, Alexander, et al.
Published: (2023)
by: Bukharin, Alexander, et al.
Published: (2023)
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
by: Lin, Yu-Chen, et al.
Published: (2024)
by: Lin, Yu-Chen, et al.
Published: (2024)
Prior Prompt Engineering for Reinforcement Fine-Tuning
by: Taveekitworachai, Pittawat, et al.
Published: (2025)
by: Taveekitworachai, Pittawat, et al.
Published: (2025)
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
by: Soylu, Dilara, et al.
Published: (2024)
by: Soylu, Dilara, et al.
Published: (2024)
CAFE: Retrieval Head-based Coarse-to-Fine Information Seeking to Enhance Multi-Document QA Capability
by: Peng, Han, et al.
Published: (2025)
by: Peng, Han, et al.
Published: (2025)
Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG
by: Jang, Jisoo, et al.
Published: (2025)
by: Jang, Jisoo, et al.
Published: (2025)
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning
by: Shi, Zhengxiang, et al.
Published: (2023)
by: Shi, Zhengxiang, et al.
Published: (2023)
Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs
by: Jindal, Ishan, et al.
Published: (2024)
by: Jindal, Ishan, et al.
Published: (2024)
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
by: Lai, Song, et al.
Published: (2025)
by: Lai, Song, et al.
Published: (2025)
Toward Secure Tuning: Mitigating Security Risks from Instruction Fine-Tuning
by: Du, Yanrui, et al.
Published: (2024)
by: Du, Yanrui, et al.
Published: (2024)
Optimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks
by: Hu, Jiacheng, et al.
Published: (2024)
by: Hu, Jiacheng, et al.
Published: (2024)
In-Context Examples Matter: Improving Emotion Recognition in Conversation with Instruction Tuning
by: Ma, Hui, et al.
Published: (2025)
by: Ma, Hui, et al.
Published: (2025)
Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)
by: Zhu, Wenhong, et al.
Published: (2025)
On the Relationship between Skill Neurons and Robustness in Prompt Tuning
by: Ackermann, Leon, et al.
Published: (2023)
by: Ackermann, Leon, et al.
Published: (2023)
QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
by: Liu, Wanlong, et al.
Published: (2025)
by: Liu, Wanlong, et al.
Published: (2025)
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
by: Sun, Chengwei, et al.
Published: (2024)
by: Sun, Chengwei, et al.
Published: (2024)
Evaluating Prompt-Based and Fine-Tuned Approaches to Czech Anaphora Resolution
by: Stano, Patrik, et al.
Published: (2025)
by: Stano, Patrik, et al.
Published: (2025)
Similar Items
-
No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
by: Shi, Wenhang, et al.
Published: (2025) -
Scaling Agentic Capabilities via Grounded Interaction Synthesis
by: Shi, Wenhang, et al.
Published: (2026) -
Investigating the Impact of Rationales for LLMs on Natural Language Understanding
by: Shi, Wenhang, et al.
Published: (2025) -
Joint Knowledge Editing for Information Enrichment and Probability Promotion
by: Shi, Wenhang, et al.
Published: (2024) -
FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics
by: Du, Yupei, et al.
Published: (2023)