Imagen de Portada

The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Riemer, Matthew, Miehling, Erik, Liu, Miao, Bouneffouf, Djallel, Campbell, Murray
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Position: Theory of Mind Benchmarks are Broken for Large Language Models
por: Riemer, Matthew, et al.
Publicado: (2024)

The Ultimate Test of Superintelligent AI Agents: Can an AI Balance Care and Control in Asymmetric Relationships?
por: Bouneffouf, Djallel, et al.
Publicado: (2025)

Survey: Multi-Armed Bandits Meet Large Language Models
por: Bouneffouf, Djallel, et al.
Publicado: (2025)

CELL your Model: Contrastive Explanations for Large Language Models
por: Luss, Ronny, et al.
Publicado: (2024)

Evaluating the Prompt Steerability of Large Language Models
por: Miehling, Erik, et al.
Publicado: (2024)

Revisiting Replay and Gradient Alignment for Continual Pre-Training of Large Language Models
por: Abbes, Istabrak, et al.
Publicado: (2025)

Preserving Diversity in Supervised Fine-Tuning of Large Language Models
por: Li, Ziniu, et al.
Publicado: (2024)

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
por: Wei, Quan, et al.
Publicado: (2025)

SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
por: Kim, Gyuhak, et al.
Publicado: (2025)

COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling
por: Lin, Baihan, et al.
Publicado: (2024)

A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models
por: Chai, Yidong, et al.
Publicado: (2025)

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
por: Fang, Zihan, et al.
Publicado: (2024)

Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
por: Zhang, Minmin, et al.
Publicado: (2026)

Crafting Efficient Fine-Tuning Strategies for Large Language Models
por: Oliver, Michael, et al.
Publicado: (2024)

FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Models
por: Yang, Guohao, et al.
Publicado: (2026)

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
por: Zhang, Longteng, et al.
Publicado: (2026)

Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
por: Xu, Yichen, et al.
Publicado: (2026)

Scopes of Alignment
por: Varshney, Kush R., et al.
Publicado: (2025)

Linearization Explains Fine-Tuning in Large Language Models
por: Afzal, Zahra Rahimi, et al.
Publicado: (2026)

TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
por: Chen, Xiangyu, et al.
Publicado: (2025)

Efficient Inference Using Large Language Models with Limited Human Data: Fine-Tuning then Rectification
por: Wang, Lei, et al.
Publicado: (2025)

On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
por: Wang, Shumin, et al.
Publicado: (2026)

PrivTune: Efficient and Privacy-Preserving Fine-Tuning of Large Language Models via Device-Cloud Collaboration
por: Liu, Yi, et al.
Publicado: (2025)

Rotation-Preserving Supervised Fine-Tuning
por: Jin, Hangzhan, et al.
Publicado: (2026)

Proximal Supervised Fine-Tuning
por: Zhu, Wenhong, et al.
Publicado: (2025)

Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
por: Wang, Shilong, et al.
Publicado: (2025)

LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
por: Zhang, Yuanhe, et al.
Publicado: (2025)

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
por: Zhou, Jiajun, et al.
Publicado: (2025)

MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning
por: Lu, Yiyang, et al.
Publicado: (2026)

Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
por: Uehara, Masatoshi, et al.
Publicado: (2024)

Bi-LoRA: Efficient Sharpness-Aware Minimization for Fine-Tuning Large-Scale Models
por: Liu, Yuhang, et al.
Publicado: (2025)

Unlock the Correlation between Supervised Fine-Tuning and Reinforcement Learning in Training Code Large Language Models
por: Chen, Jie, et al.
Publicado: (2024)

ROER: Regularized Optimal Experience Replay
por: Li, Changling, et al.
Publicado: (2024)

Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
por: Zhan, Huixin, et al.
Publicado: (2024)

Reinforcement Learning Fine-Tunes a Sparse Subnetwork in Large Language Models
por: Balashov, Andrii
Publicado: (2025)

BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models
por: Chang, Aofei, et al.
Publicado: (2024)

Boosting Large Language Models with Mask Fine-Tuning
por: Zhang, Mingyuan, et al.
Publicado: (2025)

Scaling Sparse Fine-Tuning to Large Language Models
por: Ansell, Alan, et al.
Publicado: (2024)

Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning
por: Hong, Joey, et al.
Publicado: (2024)

Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
por: Hu, Senkang, et al.
Publicado: (2025)