Guardado en:
| Autores principales: | Dong, Jiancheng, Jiang, Lei, Jin, Wei, Cheng, Lu |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2408.09327 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
por: Wang, Shuhe, et al.
Publicado: (2024)
por: Wang, Shuhe, et al.
Publicado: (2024)
Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
por: Huang, Zeyu, et al.
Publicado: (2025)
por: Huang, Zeyu, et al.
Publicado: (2025)
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
por: Lin, Zekai, et al.
Publicado: (2026)
por: Lin, Zekai, et al.
Publicado: (2026)
Anchored Supervised Fine-Tuning
por: Zhu, He, et al.
Publicado: (2025)
por: Zhu, He, et al.
Publicado: (2025)
FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics
por: Du, Yupei, et al.
Publicado: (2023)
por: Du, Yupei, et al.
Publicado: (2023)
Proximal Supervised Fine-Tuning
por: Zhu, Wenhong, et al.
Publicado: (2025)
por: Zhu, Wenhong, et al.
Publicado: (2025)
UFT: Unifying Supervised and Reinforcement Fine-Tuning
por: Liu, Mingyang, et al.
Publicado: (2025)
por: Liu, Mingyang, et al.
Publicado: (2025)
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning
por: Hanny, David, et al.
Publicado: (2024)
por: Hanny, David, et al.
Publicado: (2024)
Near-Policy: Accelerating On-Policy Distillation via Asynchronous Generation and Selective Packing
por: Rang, Miao, et al.
Publicado: (2026)
por: Rang, Miao, et al.
Publicado: (2026)
API Pack: A Massive Multi-Programming Language Dataset for API Call Generation
por: Guo, Zhen, et al.
Publicado: (2024)
por: Guo, Zhen, et al.
Publicado: (2024)
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
por: Yu, Yongcan, et al.
Publicado: (2025)
por: Yu, Yongcan, et al.
Publicado: (2025)
Knowledge Fusion of Large Language Models Via Modular SkillPacks
por: Du, Guodong, et al.
Publicado: (2025)
por: Du, Guodong, et al.
Publicado: (2025)
Supervised Fine-Tuning as Inverse Reinforcement Learning
por: Sun, Hao
Publicado: (2024)
por: Sun, Hao
Publicado: (2024)
Mind the Gap: Data Rewriting for Stable Off-Policy Supervised Fine-Tuning
por: Zhao, Shiwan, et al.
Publicado: (2025)
por: Zhao, Shiwan, et al.
Publicado: (2025)
Effect of Document Packing on the Latent Multi-Hop Reasoning Capabilities of Large Language Models
por: Prato, Gabriele, et al.
Publicado: (2025)
por: Prato, Gabriele, et al.
Publicado: (2025)
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
por: Fu, Yuqian, et al.
Publicado: (2025)
por: Fu, Yuqian, et al.
Publicado: (2025)
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
por: Wang, Fangxin, et al.
Publicado: (2026)
por: Wang, Fangxin, et al.
Publicado: (2026)
QEFT: Quantization for Efficient Fine-Tuning of LLMs
por: Lee, Changhun, et al.
Publicado: (2024)
por: Lee, Changhun, et al.
Publicado: (2024)
Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority
por: Shen, Zhanming, et al.
Publicado: (2026)
por: Shen, Zhanming, et al.
Publicado: (2026)
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
por: Sun, Chengwei, et al.
Publicado: (2024)
por: Sun, Chengwei, et al.
Publicado: (2024)
Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
por: Yano, Kazuki, et al.
Publicado: (2026)
por: Yano, Kazuki, et al.
Publicado: (2026)
From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
por: Wu, Haodong, et al.
Publicado: (2026)
por: Wu, Haodong, et al.
Publicado: (2026)
Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning
por: Feng, Weitao, et al.
Publicado: (2025)
por: Feng, Weitao, et al.
Publicado: (2025)
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
por: Lai, Song, et al.
Publicado: (2025)
por: Lai, Song, et al.
Publicado: (2025)
FisherSFT: Data-Efficient Supervised Fine-Tuning of Language Models Using Information Gain
por: Deb, Rohan, et al.
Publicado: (2025)
por: Deb, Rohan, et al.
Publicado: (2025)
FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
por: Bian, Jieming, et al.
Publicado: (2025)
por: Bian, Jieming, et al.
Publicado: (2025)
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA
por: Wang, Zhichao
Publicado: (2025)
por: Wang, Zhichao
Publicado: (2025)
Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
por: Rallapalli, Swati, et al.
Publicado: (2025)
por: Rallapalli, Swati, et al.
Publicado: (2025)
Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control
por: Wang, Xinyu, et al.
Publicado: (2026)
por: Wang, Xinyu, et al.
Publicado: (2026)
IA2: Alignment with ICL Activations Improves Supervised Fine-Tuning
por: Mishra, Aayush, et al.
Publicado: (2025)
por: Mishra, Aayush, et al.
Publicado: (2025)
JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning
por: Tahir, Anique, et al.
Publicado: (2024)
por: Tahir, Anique, et al.
Publicado: (2024)
Supervised Fine-Tuning Achieve Rapid Task Adaption Via Alternating Attention Head Activation Patterns
por: Zhao, Yang, et al.
Publicado: (2024)
por: Zhao, Yang, et al.
Publicado: (2024)
Filtering Beats Fine Tuning: A Bayesian Kalman View of In Context Learning in LLMs
por: Kiruluta, Andrew
Publicado: (2026)
por: Kiruluta, Andrew
Publicado: (2026)
Parameter-Efficient Fine-Tuning via Circular Convolution
por: Chen, Aochuan, et al.
Publicado: (2024)
por: Chen, Aochuan, et al.
Publicado: (2024)
Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models
por: Doering, Nigel, et al.
Publicado: (2024)
por: Doering, Nigel, et al.
Publicado: (2024)
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
por: Hong, Joey, et al.
Publicado: (2024)
por: Hong, Joey, et al.
Publicado: (2024)
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
por: Jiang, Ting, et al.
Publicado: (2024)
por: Jiang, Ting, et al.
Publicado: (2024)
LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning
por: Liu, Zihang, et al.
Publicado: (2025)
por: Liu, Zihang, et al.
Publicado: (2025)
Enhancing Automated Essay Scoring with Three Techniques: Two-Stage Fine-Tuning, Score Alignment, and Self-Training
por: Choi, Hongseok, et al.
Publicado: (2026)
por: Choi, Hongseok, et al.
Publicado: (2026)
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
por: Dong, Guanting, et al.
Publicado: (2023)
por: Dong, Guanting, et al.
Publicado: (2023)
Ejemplares similares
-
Packing Analysis: Packing Is More Appropriate for Large Models or Datasets in Supervised Fine-tuning
por: Wang, Shuhe, et al.
Publicado: (2024) -
Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
por: Huang, Zeyu, et al.
Publicado: (2025) -
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
por: Lin, Zekai, et al.
Publicado: (2026) -
Anchored Supervised Fine-Tuning
por: Zhu, He, et al.
Publicado: (2025) -
FTFT: Efficient and Robust Fine-Tuning by Transferring Training Dynamics
por: Du, Yupei, et al.
Publicado: (2023)