Cover Image

Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Yifang, Zhu, David, Du, Simon, Jamieson, Kevin, Liu, Yang
Format:	Preprint
Published:	2024
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

Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning
by: Chen, Yifang, et al.
Published: (2024)

Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
by: Li, Yaxuan, et al.
Published: (2026)

LongSkywork: A Training Recipe for Efficiently Extending Context Length in Large Language Models
by: Zhao, Liang, et al.
Published: (2024)

Enhancing Action and Ingredient Modeling for Semantically Grounded Recipe Generation
by: Liu, Guoshan, et al.
Published: (2026)

InfiR2: A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models
by: Wang, Wenjun, et al.
Published: (2025)

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
by: Bhatt, Gantavya, et al.
Published: (2024)

MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
by: Zhao, Changsheng, et al.
Published: (2025)

DataChef: Cooking Up Optimal Data Recipes for LLM Adaptation via Reinforcement Learning
by: Chen, Yicheng, et al.
Published: (2026)

Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages
by: Chen, Zui, et al.
Published: (2025)

Activation Scaling for Steering and Interpreting Language Models
by: Stoehr, Niklas, et al.
Published: (2024)

VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo
by: Ma, Qianli, et al.
Published: (2025)

MAMM-Refine: A Recipe for Improving Faithfulness in Generation with Multi-Agent Collaboration
by: Wan, David, et al.
Published: (2025)

Rethinking Chain-of-Thought from the Perspective of Self-Training
by: Wu, Zongqian, et al.
Published: (2024)

Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study
by: Vij, Anneketh, et al.
Published: (2025)

SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding
by: Cai, Songcheng, et al.
Published: (2026)

Rethinking Reflection in Pre-Training
by: AI, Essential, et al.
Published: (2025)

Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents
by: Zhang, Xing, et al.
Published: (2026)

Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning
by: Wang, Yiping, et al.
Published: (2024)

Rethinking Interpretability in the Era of Large Language Models
by: Singh, Chandan, et al.
Published: (2024)

Data-Constrained Synthesis of Training Data for De-Identification
by: Vakili, Thomas, et al.
Published: (2025)

Data Management For Training Large Language Models: A Survey
by: Wang, Zige, et al.
Published: (2023)

AllMem: A Memory-centric Recipe for Efficient Long-context Modeling
by: Wang, Ziming, et al.
Published: (2026)

Rethinking Data Mixing from the Perspective of Large Language Models
by: Xu, Yuanjian, et al.
Published: (2026)

OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe
by: Zhang, Kaichen, et al.
Published: (2025)

Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology
by: Tran, Son Quoc, et al.
Published: (2024)

HunyuanProver: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving
by: Li, Yang, et al.
Published: (2024)

Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs
by: Golde, Jonas, et al.
Published: (2023)

OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data
by: Du, Yuwen, et al.
Published: (2026)

Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
by: Pan, Bowen, et al.
Published: (2024)

ActionStudio: A Lightweight Framework for Data and Training of Large Action Models
by: Zhang, Jianguo, et al.
Published: (2025)

JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
by: Zhou, Kun, et al.
Published: (2024)

MASE: Interpretable NLP Models via Model-Agnostic Saliency Estimation
by: Yang, Zhou, et al.
Published: (2025)

Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation
by: He, Bowei, et al.
Published: (2026)

Fine-Grained Interpretation of Political Opinions in Large Language Models
by: Hu, Jingyu, et al.
Published: (2025)

A Recipe For Building a Compliant Real Estate Chatbot
by: Madani, Navid, et al.
Published: (2024)

Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models
by: Zhu, Zhaowei, et al.
Published: (2023)

Mechanistic Data Attribution: Tracing the Training Origins of Interpretable LLM Units
by: Chen, Jianhui, et al.
Published: (2026)

An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning
by: Sun, Wei, et al.
Published: (2025)

The Aloe Family Recipe for Open and Specialized Healthcare LLMs
by: Garcia-Gasulla, Dario, et al.
Published: (2025)

PersonaMatrix: A Recipe for Persona-Aware Evaluation of Legal Summarization
by: Pang, Tsz Fung, et al.
Published: (2025)