Cover Image

Overtrained Language Models Are Harder to Fine-Tune

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Springer, Jacob Mitchell, Goyal, Sachin, Wen, Kaiyue, Kumar, Tanishq, Yue, Xiang, Malladi, Sadhika, Neubig, Graham, Raghunathan, Aditi
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Repetition Improves Language Model Embeddings
by: Springer, Jacob Mitchell, et al.
Published: (2024)

Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
by: Watts, Ishaan, et al.
Published: (2026)

Understanding Catastrophic Forgetting in Language Models via Implicit Inference
by: Kotha, Suhas, et al.
Published: (2023)

Mode-Conditioning Unlocks Superior Test-Time Scaling
by: Wu, Chen Henry, et al.
Published: (2025)

LESS: Selecting Influential Data for Targeted Instruction Tuning
by: Xia, Mengzhou, et al.
Published: (2024)

Fine-Tuning Language Models with Just Forward Passes
by: Malladi, Sadhika, et al.
Published: (2023)

Understanding the Influence of Synthetic Data for Text Embedders
by: Springer, Jacob Mitchell, et al.
Published: (2025)

Mitigating Bias in RAG: Controlling the Embedder
by: Kim, Taeyoun, et al.
Published: (2025)

Weight Ensembling Improves Reasoning in Language Models
by: Dang, Xingyu, et al.
Published: (2025)

On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Language Models
by: Zhang, Charlie, et al.
Published: (2025)

Context-Parametric Inversion: Why Instruction Finetuning Can Worsen Context Reliance
by: Goyal, Sachin, et al.
Published: (2024)

Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions
by: Liu, Emmy, et al.
Published: (2025)

Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization
by: Razin, Noam, et al.
Published: (2024)

MUSE: Machine Unlearning Six-Way Evaluation for Language Models
by: Shi, Weijia, et al.
Published: (2024)

Language Modeling with Editable External Knowledge
by: Li, Belinda Z., et al.
Published: (2024)

BehaviorBox: Automated Discovery of Fine-Grained Performance Differences Between Language Models
by: Tjuatja, Lindia, et al.
Published: (2025)

Early Data Exposure Improves Robustness to Subsequent Fine-Tuning
by: Feng, Lawrence, et al.
Published: (2026)

Metadata Conditioning Accelerates Language Model Pre-training
by: Gao, Tianyu, et al.
Published: (2025)

Self-Trained Verification for Training- and Test-Time Self-Improvement
by: Wu, Chen Henry, et al.
Published: (2026)

Effective Strategies for Asynchronous Software Engineering Agents
by: Geng, Jiayi, et al.
Published: (2026)

Trainable Transformer in Transformer
by: Panigrahi, Abhishek, et al.
Published: (2023)

Can Large Language Models be Trusted for Evaluation? Scalable Meta-Evaluation of LLMs as Evaluators via Agent Debate
by: Chern, Steffi, et al.
Published: (2024)

Preference Learning Algorithms Do Not Learn Preference Rankings
by: Chen, Angelica, et al.
Published: (2024)

An Incomplete Loop: Instruction Inference, Instruction Following, and In-context Learning in Language Models
by: Liu, Emmy, et al.
Published: (2024)

Natural Language Fine-Tuning
by: Liu, Jia, et al.
Published: (2024)

What Are Tools Anyway? A Survey from the Language Model Perspective
by: Wang, Zhiruo, et al.
Published: (2024)

ZINA: Multimodal Fine-grained Hallucination Detection and Editing
by: Wada, Yuiga, et al.
Published: (2025)

Sharpness-Aware Minimization Enhances Feature Quality via Balanced Learning
by: Springer, Jacob Mitchell, et al.
Published: (2024)

Meta-Tool: Efficient Few-Shot Tool Adaptation for Small Language Models
by: Kumar, Sachin
Published: (2026)

Reusing Overtrained Language Models Saturates Scaling
by: Liew, Seng Pei, et al.
Published: (2025)

G-Loss: Graph-Guided Fine-Tuning of Language Models
by: Sharma, Aditya, et al.
Published: (2026)

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

Provable unlearning in topic modeling and downstream tasks
by: Wei, Stanley, et al.
Published: (2024)

LLMs for Explainable Business Decision-Making: A Reinforcement Learning Fine-Tuning Approach
by: Cheng, Xiang, et al.
Published: (2025)

Flow Map Language Models: One-step Language Modeling via Continuous Denoising
by: Lee, Chanhyuk, et al.
Published: (2026)

Memorization in Fine-Tuned Large Language Models
by: Savine, Danil
Published: (2025)

In Good GRACEs: Principled Teacher Selection for Knowledge Distillation
by: Panigrahi, Abhishek, et al.
Published: (2025)

T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
by: Maini, Pratyush, et al.
Published: (2023)

The Coverage Principle: How Pre-Training Enables Post-Training
by: Chen, Fan, et al.
Published: (2025)

Think before you speak: Training Language Models With Pause Tokens
by: Goyal, Sachin, et al.
Published: (2023)