Accelerate Scaling of LLM Finetuning via Quantifying the Coverage and Depth of Instruction Set
Fuente:
arXiv
Saved in:
| Main Authors: | Wu, Chengwei, Du, Li, Zhao, Hanyu, Ju, Yiming, Wang, Jiapu, Chen, Tianyu, Zhou, Haoyi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency
by: Zhao, Hanyu, et al.
Published: (2024)
by: Zhao, Hanyu, et al.
Published: (2024)
Scaling Towards the Information Boundary of Instruction Sets: The Infinity Instruct Subject Technical Report
by: Du, Li, et al.
Published: (2025)
by: Du, Li, et al.
Published: (2025)
CDTP: A Large-Scale Chinese Data-Text Pair Dataset for Comprehensive Evaluation of Chinese LLMs
by: Wu, Chengwei, et al.
Published: (2025)
by: Wu, Chengwei, et al.
Published: (2025)
Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
by: Zhuo, Xingrui, et al.
Published: (2024)
by: Zhuo, Xingrui, et al.
Published: (2024)
TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning
by: Ma, Simin, et al.
Published: (2025)
by: Ma, Simin, et al.
Published: (2025)
Instructing the Architecture Search for Spatial-temporal Sequence Forecasting with LLM
by: Xue, Xin, et al.
Published: (2025)
by: Xue, Xin, et al.
Published: (2025)
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
by: Zhao, Chenyang, et al.
Published: (2024)
by: Zhao, Chenyang, et al.
Published: (2024)
Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models
by: Li, Jijie, et al.
Published: (2025)
by: Li, Jijie, et al.
Published: (2025)
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets
by: Indurthi, Sathish Reddy, et al.
Published: (2024)
by: Indurthi, Sathish Reddy, et al.
Published: (2024)
Large Language Models to Diffusion Finetuning
by: Cetin, Edoardo, et al.
Published: (2025)
by: Cetin, Edoardo, et al.
Published: (2025)
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
by: Zhang, Bo-Wen, et al.
Published: (2024)
by: Zhang, Bo-Wen, et al.
Published: (2024)
HyFunc: Accelerating LLM-based Function Calls for Agentic AI through Hybrid-Model Cascade and Dynamic Templating
by: Liao, Weibin, et al.
Published: (2026)
by: Liao, Weibin, et al.
Published: (2026)
Beyond Parameters: Exploring Virtual Logic Depth for Scaling Laws
by: Zhu, Ruike, et al.
Published: (2025)
by: Zhu, Ruike, et al.
Published: (2025)
BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuning
by: Shen, Qianli, et al.
Published: (2025)
by: Shen, Qianli, et al.
Published: (2025)
Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extraction
by: Jing, Xiaonan, et al.
Published: (2026)
by: Jing, Xiaonan, et al.
Published: (2026)
LLM-DSE: Searching Accelerator Parameters with LLM Agents
by: Wang, Hanyu, et al.
Published: (2025)
by: Wang, Hanyu, et al.
Published: (2025)
LLM-Inspired Pretrain-Then-Finetune for Small-Data, Large-Scale Optimization
by: Zhang, Zishi, et al.
Published: (2026)
by: Zhang, Zishi, et al.
Published: (2026)
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
by: Chen, Shuangyi, et al.
Published: (2025)
by: Chen, Shuangyi, et al.
Published: (2025)
On Instruction-Finetuning Neural Machine Translation Models
by: Raunak, Vikas, et al.
Published: (2024)
by: Raunak, Vikas, et al.
Published: (2024)
LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation
by: Lee, Suhyeon, et al.
Published: (2023)
by: Lee, Suhyeon, et al.
Published: (2023)
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
by: Ye, Jiasheng, et al.
Published: (2023)
by: Ye, Jiasheng, et al.
Published: (2023)
FRoD: Full-Rank Efficient Fine-Tuning with Rotational Degrees for Fast Convergence
by: Wan, Guoan, et al.
Published: (2025)
by: Wan, Guoan, et al.
Published: (2025)
Multimodal Web Navigation with Instruction-Finetuned Foundation Models
by: Furuta, Hiroki, et al.
Published: (2023)
by: Furuta, Hiroki, et al.
Published: (2023)
Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning
by: Yadav, Abhay
Published: (2026)
by: Yadav, Abhay
Published: (2026)
SDFP: Speculative Decoding with FIT-Pruned Models for Training-Free and Plug-and-Play LLM Acceleration
by: Wei, Hanyu, et al.
Published: (2026)
by: Wei, Hanyu, et al.
Published: (2026)
Ctrl-VI: Controllable Video Synthesis via Variational Inference
by: Duan, Haoyi, et al.
Published: (2025)
by: Duan, Haoyi, et al.
Published: (2025)
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs
by: Hu, Zichao, et al.
Published: (2024)
by: Hu, Zichao, et al.
Published: (2024)
Temporal Binding Foundation Model for Material Property Recognition via Tactile Sequence Perception
by: You, Hengxu, et al.
Published: (2025)
by: You, Hengxu, et al.
Published: (2025)
Accelerating Multi-modal LLM Gaming Performance via Input Prediction and Mishit Correction
by: Lin, Ziyang, et al.
Published: (2025)
by: Lin, Ziyang, et al.
Published: (2025)
Learning Dynamics of LLM Finetuning
by: Ren, Yi, et al.
Published: (2024)
by: Ren, Yi, et al.
Published: (2024)
Finetuning Text-to-Image Diffusion Models for Fairness
by: Shen, Xudong, et al.
Published: (2023)
by: Shen, Xudong, et al.
Published: (2023)
The Curse of Helpfulness: Inverse Scaling Law in Robustness to Distractor Instructions via DistractionIF
by: Su, Zeli, et al.
Published: (2026)
by: Su, Zeli, et al.
Published: (2026)
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation
by: Li, Renhao, et al.
Published: (2024)
by: Li, Renhao, et al.
Published: (2024)
AIR: Complex Instruction Generation via Automatic Iterative Refinement
by: Liu, Wei, et al.
Published: (2025)
by: Liu, Wei, et al.
Published: (2025)
Holdout-Loss-Based Data Selection for LLM Finetuning via In-Context Learning
by: Zhang, Ling, et al.
Published: (2025)
by: Zhang, Ling, et al.
Published: (2025)
Closing the Expression Gap in LLM Instructions via Socratic Questioning
by: Sun, Jianwen, et al.
Published: (2025)
by: Sun, Jianwen, et al.
Published: (2025)
Unifying Large Language Models and Knowledge Graphs: A Roadmap
by: Pan, Shirui, et al.
Published: (2023)
by: Pan, Shirui, et al.
Published: (2023)
Towards Long-window Anchoring in Vision-Language Model Distillation
by: Zhou, Haoyi, et al.
Published: (2025)
by: Zhou, Haoyi, et al.
Published: (2025)
Aligning Instruction Tuning with Pre-training
by: Liang, Yiming, et al.
Published: (2025)
by: Liang, Yiming, et al.
Published: (2025)
Deception at Scale: Deceptive Designs in 1K LLM-Generated Ecommerce Components
by: Chen, Ziwei, et al.
Published: (2025)
by: Chen, Ziwei, et al.
Published: (2025)
Similar Items
-
Beyond IID: Optimizing Instruction Learning from the Perspective of Instruction Interaction and Dependency
by: Zhao, Hanyu, et al.
Published: (2024) -
Scaling Towards the Information Boundary of Instruction Sets: The Infinity Instruct Subject Technical Report
by: Du, Li, et al.
Published: (2025) -
CDTP: A Large-Scale Chinese Data-Text Pair Dataset for Comprehensive Evaluation of Chinese LLMs
by: Wu, Chengwei, et al.
Published: (2025) -
Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
by: Zhuo, Xingrui, et al.
Published: (2024) -
TCIA: A Task-Centric Instruction Augmentation Method for Instruction Finetuning
by: Ma, Simin, et al.
Published: (2025)