Cover Image

Relative-Based Scaling Law for Neural Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yue, Baoqing, Zhou, Jinyuan, Wei, Zixi, Zhan, Jingtao, Ai, Qingyao, Liu, Yiqun
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

What Scales in Cross-Entropy Scaling Law?
by: Yan, Junxi, et al.
Published: (2025)

Option-ID Based Elimination For Multiple Choice Questions
by: Zhu, Zhenhao, et al.
Published: (2025)

Beyond Scalar Reward Model: Learning Generative Judge from Preference Data
by: Ye, Ziyi, et al.
Published: (2024)

Capability-aware Prompt Reformulation Learning for Text-to-Image Generation
by: Zhan, Jingtao, et al.
Published: (2024)

Improve Large Language Model Systems with User Logs
by: Wang, Changyue, et al.
Published: (2026)

Evaluating Intelligence via Trial and Error
by: Zhan, Jingtao, et al.
Published: (2025)

Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
by: Ye, Jiasheng, et al.
Published: (2024)

Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On
by: Zeng, Liang, et al.
Published: (2024)

Provable Scaling Laws for the Test-Time Compute of Large Language Models
by: Chen, Yanxi, et al.
Published: (2024)

Can Language Models Discover Scaling Laws?
by: Lin, Haowei, et al.
Published: (2025)

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
by: Zhou, Chenxi, et al.
Published: (2025)

Observational Scaling Laws and the Predictability of Language Model Performance
by: Ruan, Yangjun, et al.
Published: (2024)

Uncovering Scaling Laws for Large Language Models via Inverse Problems
by: Verma, Arun, et al.
Published: (2025)

Reasoning: From Reflection to Solution
by: Li, Zixi
Published: (2025)

Exploring Scaling Laws for EHR Foundation Models
by: Zhang, Sheng, et al.
Published: (2025)

Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
by: Shou, Yuntao, et al.
Published: (2024)

Query Augmentation by Decoding Semantics from Brain Signals
by: Ye, Ziyi, et al.
Published: (2024)

Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws
by: Allen-Zhu, Zeyuan, et al.
Published: (2024)

Selecting Large Language Model to Fine-tune via Rectified Scaling Law
by: Lin, Haowei, et al.
Published: (2024)

Foundations of GenIR
by: Ai, Qingyao, et al.
Published: (2025)

Graphical Reasoning: LLM-based Semi-Open Relation Extraction
by: Tao, Yicheng, et al.
Published: (2024)

Theoretical Foundations of Scaling Law in Familial Models
by: Song, Huan, et al.
Published: (2025)

P$^2$ Law: Scaling Law for Post-Training After Model Pruning
by: Chen, Xiaodong, et al.
Published: (2024)

Distillation Scaling Laws
by: Busbridge, Dan, et al.
Published: (2025)

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
by: Su, Weihang, et al.
Published: (2024)

ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMs
by: Li, Xuancheng, et al.
Published: (2026)

Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing
by: Wang, Changyue, et al.
Published: (2025)

Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
by: Yang, Zihua, et al.
Published: (2026)

CarbonScaling: Extending Neural Scaling Laws for Carbon Footprint in Large Language Models
by: Jiang, Lei, et al.
Published: (2025)

JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
by: Su, Weihang, et al.
Published: (2025)

Simple Policy Gradients for Reasoning with Diffusion Language Models
by: Zhan, Anthony
Published: (2025)

Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
by: Rafailov, Rafael, et al.
Published: (2024)

Scaling Laws For Dense Retrieval
by: Fang, Yan, et al.
Published: (2024)

Beyond Experience Retrieval: Learning to Generate Utility-Optimized Structured Experience for Frozen LLMs
by: Li, Xuancheng, et al.
Published: (2026)

To Memorize or to Retrieve: Scaling Laws for RAG-Considerate Pretraining
by: Singh, Karan, et al.
Published: (2026)

A Tale of Tails: Model Collapse as a Change of Scaling Laws
by: Dohmatob, Elvis, et al.
Published: (2024)

Scaling Law with Learning Rate Annealing
by: Tissue, Howe, et al.
Published: (2024)

Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data
by: Havrilla, Alex, et al.
Published: (2024)

Towards the Law of Capacity Gap in Distilling Language Models
by: Zhang, Chen, et al.
Published: (2023)

PhySense: Principle-Based Physics Reasoning Benchmarking for Large Language Models
by: Xu, Yinggan, et al.
Published: (2025)