Saved in:
| Main Authors: | Yu, Hongzhou, Cheng, Tianhao, Wang, Yingwen, He, Wen, Wang, Qing, Cheng, Ying, Zhang, Yuejie, Feng, Rui, Zhang, Xiaobo |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.09213 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart
by: Zhao, Bowen, et al.
Published: (2024)
by: Zhao, Bowen, 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)
Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)
by: Zhu, Wenhong, et al.
Published: (2025)
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)
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images
by: Liu, Yihang, et al.
Published: (2025)
by: Liu, Yihang, et al.
Published: (2025)
MedBioLM: Optimizing Medical and Biological QA with Fine-Tuned Large Language Models and Retrieval-Augmented Generation
by: Kim, Seonok
Published: (2025)
by: Kim, Seonok
Published: (2025)
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
by: Yu, Yongcan, et al.
Published: (2025)
by: Yu, Yongcan, et al.
Published: (2025)
MedPRMBench: A Fine-grained Benchmark for Process Reward Models in Medical Reasoning
by: Wu, Lingyan, et al.
Published: (2026)
by: Wu, Lingyan, et al.
Published: (2026)
PAFT: A Parallel Training Paradigm for Effective LLM Fine-Tuning
by: Pentyala, Shiva Kumar, et al.
Published: (2024)
by: Pentyala, Shiva Kumar, et al.
Published: (2024)
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)
MedFILIP: Medical Fine-grained Language-Image Pre-training
by: Liang, Xinjie, et al.
Published: (2025)
by: Liang, Xinjie, et al.
Published: (2025)
On-Policy Supervised Fine-Tuning for Efficient Reasoning
by: Zhao, Anhao, et al.
Published: (2026)
by: Zhao, Anhao, 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)
Anchored Supervised Fine-Tuning
by: Zhu, He, et al.
Published: (2025)
by: Zhu, He, et al.
Published: (2025)
Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control
by: Wang, Xinyu, et al.
Published: (2026)
by: Wang, Xinyu, et al.
Published: (2026)
Remote Training in Task-Oriented Communication: Supervised or Self-Supervised with Fine-Tuning?
by: Li, Hongru, et al.
Published: (2025)
by: Li, Hongru, et al.
Published: (2025)
Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods
by: Hao, Yifan, et al.
Published: (2025)
by: Hao, Yifan, et al.
Published: (2025)
A Medical Multimodal Large Language Model for Pediatric Pneumonia
by: Tian, Weiwei, et al.
Published: (2024)
by: Tian, Weiwei, et al.
Published: (2024)
Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models
by: Zhang, Wuyang, et al.
Published: (2025)
by: Zhang, Wuyang, et al.
Published: (2025)
Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained Reflection
by: Yang, Zongxian, et al.
Published: (2025)
by: Yang, Zongxian, 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)
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
by: Dai, Ziqi, et al.
Published: (2025)
by: Dai, Ziqi, et al.
Published: (2025)
SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning
by: Fu, Yuqian, et al.
Published: (2025)
by: Fu, Yuqian, 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)
QuantLRM: Quantization of Large Reasoning Models via Fine-Tuning Signals
by: Zhang, Nan, et al.
Published: (2026)
by: Zhang, Nan, et al.
Published: (2026)
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
by: Peng, Zhaopeng, et al.
Published: (2024)
by: Peng, Zhaopeng, et al.
Published: (2024)
LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach
by: Cheng, Xiang, et al.
Published: (2025)
by: Cheng, Xiang, et al.
Published: (2025)
KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning
by: Yu, Peng, et al.
Published: (2024)
by: Yu, Peng, et al.
Published: (2024)
Fine-Tuning is Fine, if Calibrated
by: Mai, Zheda, et al.
Published: (2024)
by: Mai, Zheda, et al.
Published: (2024)
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
by: Li, Shijun, et al.
Published: (2026)
by: Li, Shijun, et al.
Published: (2026)
Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
by: Ma, Yuting, et al.
Published: (2026)
by: Ma, Yuting, et al.
Published: (2026)
LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarios
by: Wuzhenghong, Wen, et al.
Published: (2024)
by: Wuzhenghong, Wen, et al.
Published: (2024)
Beyond Scores: Diagnostic LLM Evaluation via Fine-Grained Abilities
by: Zhang, Xu, et al.
Published: (2026)
by: Zhang, Xu, et al.
Published: (2026)
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
by: Ye, Junjie, et al.
Published: (2025)
by: Ye, Junjie, et al.
Published: (2025)
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
by: Stap, David, et al.
Published: (2024)
by: Stap, David, et al.
Published: (2024)
MARFT: Multi-Agent Reinforcement Fine-Tuning
by: Liao, Junwei, et al.
Published: (2025)
by: Liao, Junwei, et al.
Published: (2025)
AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation
by: Li, Qingqiu, et al.
Published: (2025)
by: Li, Qingqiu, et al.
Published: (2025)
Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
by: Zhang, Lingzhe, et al.
Published: (2026)
by: Zhang, Lingzhe, et al.
Published: (2026)
Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
by: Chen, Feng, et al.
Published: (2025)
by: Chen, Feng, et al.
Published: (2025)
InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
by: Zhu, Guanghao, et al.
Published: (2025)
by: Zhu, Guanghao, et al.
Published: (2025)
Similar Items
-
CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart
by: Zhao, Bowen, et al.
Published: (2024) -
Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling
by: Huang, Zeyu, et al.
Published: (2025) -
Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025) -
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
by: Pang, Jinlong, et al.
Published: (2025) -
AFiRe: Anatomy-Driven Self-Supervised Learning for Fine-Grained Representation in Radiographic Images
by: Liu, Yihang, et al.
Published: (2025)