Scaling with Collapse: Efficient and Predictable Training of LLM Families
Fuente:
arXiv
Saved in:
| Main Authors: | Bergsma, Shane, Zhang, Bin Claire, Dey, Nolan, Muhammad, Shaheer, Gosal, Gurpreet, Hestness, Joel |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Power Lines: Scaling Laws for Weight Decay and Batch Size in LLM Pre-training
by: Bergsma, Shane, et al.
Published: (2025)
by: Bergsma, Shane, et al.
Published: (2025)
Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs
by: Bergsma, Shane, et al.
Published: (2025)
by: Bergsma, Shane, et al.
Published: (2025)
Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs
by: Bergsma, Shane, et al.
Published: (2025)
by: Bergsma, Shane, et al.
Published: (2025)
PTPP-Aware Adaptation Scaling Laws: Predicting Domain-Adaptation Performance at Unseen Pre-Training Budgets
by: Goffinet, Etienne, et al.
Published: (2025)
by: Goffinet, Etienne, et al.
Published: (2025)
Sparse maximal update parameterization: A holistic approach to sparse training dynamics
by: Dey, Nolan, et al.
Published: (2024)
by: Dey, Nolan, et al.
Published: (2024)
Don't be lazy: CompleteP enables compute-efficient deep transformers
by: Dey, Nolan, et al.
Published: (2025)
by: Dey, Nolan, et al.
Published: (2025)
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers
by: Gray, Gavia, et al.
Published: (2024)
by: Gray, Gavia, et al.
Published: (2024)
CoRPO: Adding a Correctness Bias to GRPO Improves Generalization
by: Garg, Anisha, et al.
Published: (2025)
by: Garg, Anisha, et al.
Published: (2025)
SABER: Switchable and Balanced Training for Efficient LLM Reasoning
by: Zhao, Kai, et al.
Published: (2025)
by: Zhao, Kai, et al.
Published: (2025)
OpenELM: An Efficient Language Model Family with Open Training and Inference Framework
by: Mehta, Sachin, et al.
Published: (2024)
by: Mehta, Sachin, et al.
Published: (2024)
Memory-Efficient LLM Training with Online Subspace Descent
by: Liang, Kaizhao, et al.
Published: (2024)
by: Liang, Kaizhao, et al.
Published: (2024)
InfLLM: Training-Free Long-Context Extrapolation for LLMs with an Efficient Context Memory
by: Xiao, Chaojun, et al.
Published: (2024)
by: Xiao, Chaojun, et al.
Published: (2024)
POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation
by: Qiu, Zeju, et al.
Published: (2026)
by: Qiu, Zeju, et al.
Published: (2026)
Escaping Collapse: The Strength of Weak Data for Large Language Model Training
by: Amin, Kareem, et al.
Published: (2025)
by: Amin, Kareem, et al.
Published: (2025)
A Tale of Tails: Model Collapse as a Change of Scaling Laws
by: Dohmatob, Elvis, et al.
Published: (2024)
by: Dohmatob, Elvis, et al.
Published: (2024)
ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
by: Yang, Ling, et al.
Published: (2025)
by: Yang, Ling, et al.
Published: (2025)
Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning
by: Sondej, Filip, et al.
Published: (2025)
by: Sondej, Filip, et al.
Published: (2025)
HAPO: Training Language Models to Reason Concisely via History-Aware Policy Optimization
by: Huang, Chengyu, et al.
Published: (2025)
by: Huang, Chengyu, et al.
Published: (2025)
How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
by: Seddik, Mohamed El Amine, et al.
Published: (2024)
by: Seddik, Mohamed El Amine, et al.
Published: (2024)
Theoretical Foundations of Scaling Law in Familial Models
by: Song, Huan, et al.
Published: (2025)
by: Song, Huan, et al.
Published: (2025)
Litespark Technical Report: High-Throughput, Energy-Efficient LLM Training Framework
by: Dade, Nii Osae Osae, et al.
Published: (2025)
by: Dade, Nii Osae Osae, et al.
Published: (2025)
Muon is Scalable for LLM Training
by: Liu, Jingyuan, et al.
Published: (2025)
by: Liu, Jingyuan, et al.
Published: (2025)
Survival Meets Classification: A Novel Framework for Early Risk Prediction Models of Chronic Diseases
by: Khan, Shaheer Ahmad, et al.
Published: (2026)
by: Khan, Shaheer Ahmad, et al.
Published: (2026)
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
by: Zhou, Chenxi, et al.
Published: (2026)
by: Zhou, Chenxi, et al.
Published: (2026)
MeKi: Memory-based Expert Knowledge Injection for Efficient LLM Scaling
by: Ding, Ning, et al.
Published: (2026)
by: Ding, Ning, et al.
Published: (2026)
R$^2$PO: Decoupling Training Trajectories from Inference Responses for LLM Reasoning
by: Wang, Jingchu, et al.
Published: (2026)
by: Wang, Jingchu, et al.
Published: (2026)
Better LLM Reasoning via Dual-Play
by: Zhang, Zhengxin, et al.
Published: (2025)
by: Zhang, Zhengxin, et al.
Published: (2025)
Single Parent Family: A Spectrum of Family Members from a Single Pre-Trained Foundation Model
by: Hajimolahoseini, Habib, et al.
Published: (2024)
by: Hajimolahoseini, Habib, et al.
Published: (2024)
TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
by: Lin, Weizhe, et al.
Published: (2025)
by: Lin, Weizhe, et al.
Published: (2025)
TSO: Self-Training with Scaled Preference Optimization
by: Chen, Kaihui, et al.
Published: (2024)
by: Chen, Kaihui, et al.
Published: (2024)
PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
by: Khan, Rana Muhammad Shahroz, et al.
Published: (2024)
by: Khan, Rana Muhammad Shahroz, et al.
Published: (2024)
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
by: Chen, Mengzhao, et al.
Published: (2024)
by: Chen, Mengzhao, et al.
Published: (2024)
Training Proactive and Personalized LLM Agents
by: Sun, Weiwei, et al.
Published: (2025)
by: Sun, Weiwei, et al.
Published: (2025)
Atom of Thoughts for Markov LLM Test-Time Scaling
by: Teng, Fengwei, et al.
Published: (2025)
by: Teng, Fengwei, et al.
Published: (2025)
CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation
by: Fawi, Muhammad
Published: (2024)
by: Fawi, Muhammad
Published: (2024)
AutoScale: Scale-Aware Data Mixing for Pre-Training LLMs
by: Kang, Feiyang, et al.
Published: (2024)
by: Kang, Feiyang, et al.
Published: (2024)
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
by: Huang, Wei, et al.
Published: (2024)
by: Huang, Wei, et al.
Published: (2024)
Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
by: Zhang, Ruiqi, et al.
Published: (2024)
by: Zhang, Ruiqi, et al.
Published: (2024)
KALAVAI: Predicting When Independent Specialist Fusion Works -- A Quantitative Model for Post-Hoc Cooperative LLM Training
by: Kumaresan, Ramchand
Published: (2026)
by: Kumaresan, Ramchand
Published: (2026)
P$^2$ Law: Scaling Law for Post-Training After Model Pruning
by: Chen, Xiaodong, et al.
Published: (2024)
by: Chen, Xiaodong, et al.
Published: (2024)
Similar Items
-
Power Lines: Scaling Laws for Weight Decay and Batch Size in LLM Pre-training
by: Bergsma, Shane, et al.
Published: (2025) -
Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs
by: Bergsma, Shane, et al.
Published: (2025) -
Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs
by: Bergsma, Shane, et al.
Published: (2025) -
PTPP-Aware Adaptation Scaling Laws: Predicting Domain-Adaptation Performance at Unseen Pre-Training Budgets
by: Goffinet, Etienne, et al.
Published: (2025) -
Sparse maximal update parameterization: A holistic approach to sparse training dynamics
by: Dey, Nolan, et al.
Published: (2024)