Synthetic Data Generation for Language Models

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This field studies how to create artificial text, examples, and interaction traces that teach language models new skills or provide evaluation data when human-authored examples are scarce. Common approaches include prompting teacher models, generating tasks from structured sources such as knowledge graphs and documents, building executable simulators, and using agents to create tool-use or reasoning trajectories. Filtering, symbolic or execution-based verification, and feedback from student-model performance are used to improve quality and tailor data to particular models, domains, and difficulty levels. A central tension is whether synthetic data can scale useful training without losing diversity, factual grounding, or resemblance to real human data; gains vary by task, and recursive generation can reinforce errors or cause collapse.

Latest papers

294 papers36 papers in the last four weeks, up 20% on the four weeks before. 0.2% of all new papers.

MemSteer: Turning User Histories into Supervision for Agent Memory Optimization
Oct 29, 2026Jie Huang, Cuiling Lan, Qize Yu +2LLM Agent Memory Agent Memory Management
Peking University · Microsoft Research Asia
Text Generation in Low-Resource Languages Using Human-Curated Vocabularies
Oct 27, 2026Michael Christlambert Sinanta, Muhammad Dehan Al Kautsar, Amanda Nurul Izzah +4Text Generation Synthetic Data Generation for Language Models
Independent Researcher · Mohamed bin Zayed University of Artificial Intelligence · University of Amsterdam
Rewriting Scaling Laws for Synthetic Data with Compute and Data Constraints
Oct 22, 2026Nicholas Roberts, Ahmed Ahmed, Suhas Kotha +4Synthetic Data Pretraining Language Model Scaling Laws
Princeton University · Stanford University · Jane Street
Benchmarking Large Language Models for Detecting Satirical Code-Mixed Sentences
Oct 21, 2026Răzvan-Alexandru Smădu, Ioana Buhnila, Daniela Tanase +3Code-Switching Text Classification
National University of Science and Technology POLITEHNICA Bucharest, Bucharest, Romania · Academy of Romanian Scientists, Bucharest, Romania · Center for Data Science in Humanities, Chosun University, South Korea +3
Ask to Distill: Learning to Synthesize Data for On-Policy Distillation
Oct 20, 2026Jiashuo Sun, Hao Zhang, Xueqiang Xu +9Language Model Distillation On-Policy Distillation
University of Illinois Urbana-Champaign, Urbana, IL, USA · The Hong Kong University of Science and Technology, Hong Kong SAR, China · Texas A&M University, College Station, TX, USA
Guided Diffusion for Trustworthy Synthetic Text Generation
Oct 19, 2026Qi Pang, Hanshen Xiao, Andre Manoel +4Synthetic Data Generation for Language Models Differentially Private Synthetic Data Generation
NVIDIA · Carnegie Mellon University · Purdue University
Quality-Controlled Reply and Summary Data for Post-Training Small Language Models in AI Call-Screening Agents
Oct 18, 2026Jiaqi Gan, Tsang Ng, Ankit Raj +5Small Language Models Spoken Dialogue Systems
TIRE: Top-Down Formula Instantiation with Reliable Execution for Physics Problem Synthesis
Oct 18, 2026Xiaoyu Wu, Zhaoxin Yu, Xinglin Xiao +1Synthetic Data Generation for Language Models Scientific Reasoning in Language Models
ScienceOne AI · State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences
PRECOCIAL: Pretraining with Code Curriculums via Iterative Search for Accelerated Learning
Oct 18, 2026Edward Gu, Ali Payani, Kevin EllisSynthetic Data Pretraining Synthetic Data Generation for Language Models
Cornell University · Cisco Research
LongForge: Measuring and Improving Long-Context Instruction Data via Evidence-Assertion Reasoning Graphs
Oct 16, 2026Ziyang Chen, Junlong Jia, Xing Wu +2Synthetic Data Generation for Language Models Instruction Tuning
Exploring The Empirical Privacy Landscape of Synthetic Data
Oct 15, 2026Zihao Zhao, Avi Schwarzschild, Casey Meehan +3Privacy Leakage in Language Models Privacy-Preserving Language Models
Johns Hopkins University · OpenAI
GRADS: Graph-Regulated Agentic Data Synthesis with Controllable Difficulty
Oct 15, 2026Gaocheng Yang, Ruijie Tao, Kai Ye +3Synthetic Data Generation for LLM Agents Synthetic Data Generation for Language Models
Johns Hopkins University · Nanjing University · Amazon
AFCS: Atomic-Fact-Conditioned Synthesis for Factual Knowledge Pretraining
Oct 14, 2026Yunhao Wang, Feng Zhang, Tinghao Yu +2Synthetic Data Generation for Language Models Synthetic Data Pretraining
Hunyuan Team, Tencent
Instruction Mining From Images: Constructing a Synthetic Dataset for Multimodal Learning ILSP DCVLR Competition Submission
Oct 14, 2026Eleni E. Kasoura, Dimitrios Damianos, Georgios Paraskevopoulos +1Visual Instruction Tuning Synthetic Data Generation for Language Models
Institute of Language and Speech Processing Athena Research Center Athens, GR 151 25
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph
Oct 11, 2026Jiaxin Fang, Runyuan He, Arnav Gupta +3Synthetic Benchmark Generation Benchmark Design
University of California, Berkeley
Probable but Not Valid: A Psychometric Audit of Decision Models as Synthetic Survey Respondents
Oct 10, 2026Mantas Lukauskas, Viktorija ŠarkauskaitėSynthetic Data Generation Synthetic Data Evaluation
Hostinger; Kaunas University of Technology; AI Insight Lab · AI Insight Lab; Independent Researcher
Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds
Oct 10, 2026Mengzhou Wu, Yuzhe Guo, Jiawen Tao +9LLM Agent Training Synthetic Data Generation for Language Models
School of Computer Science, Peking University, Beijing, China · Beijing Tongming Lake Information Technology Application Innovation Center, Beijing, China · WeChat, Tencent Inc., China +2
MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents
Oct 8, 2026Arkajyoti Chakraborty, Adam Zernik, Ishika Kulkarni +6Task-Oriented Dialogue Systems Adversarial Robustness of Language Models
University of California, Santa Cruz · Uniphore
MagicBeanStalk: Escaping the RLVR Zero Advantage Trap with Recursive Data Synthesis
Oct 8, 2026Ximing Lu, Aviral Kumar, Jan Kautz +2Reinforcement Learning with Verifiable Rewards Synthetic Data Generation for Language Models
University of Washington · Carnegie Mellon University · NVIDIA +1
Know What You Don't Know: Agentic Data Synthesis by Evolving Generation Space for Closed-Loop Learning
Oct 8, 2026Wei Yang, Qing Ping, Youzhi Luo +4Synthetic Data Generation LLM Agent Trajectory Synthesis
University of Southern California · AWS AI
UniData: Universal Multimodal Instruction Generation Pipeline
Oct 8, 2026Jiaqi Tang, Yi-Feng Wu, Yuting Zhang +12Synthetic Data Generation for Language Models Multimodal Generation
The Hong Kong University of Science and Technology · ATH, Alibaba Group · The Hong Kong University of Science and Technology (Guangzhou) +2
SynCo: Data Synthesis Co-Training for Self-Evolving LLMs via Multi-Agent Reinforcement Learning
Oct 8, 2026Wei Yang, Shawn Li, Yuehan Qin +8Self-Evolving Agents Synthetic Data Generation for Language Models
University of Southern California · The George Washington University · Georgia Institute of Technology
SAGE: Semantic Anchor-Guided Evolution for Grounded Medical QA Data Synthesis
Oct 6, 2026Chuan Li, Chengyu Wang, Cen Chen +3Synthetic Data Generation Biomedical QA
East China Normal University, Shanghai, China · Alibaba Group, Hangzhou, China
Synthetic Cultural Agents from Aggregate Anchors
Oct 5, 2026Augusto Gonzalez-Bonorino, Kseniia Biriukova, Monica CapraLarge Language Model-Based Social Simulation Synthetic Data Generation for Language Models
Department of Economics, Arizona State University · EconLLM Lab · Department of Information Systems, Arizona State University +1
Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training
Oct 5, 2026Zhuojing Huang, Luise Pohlmann, Lisa BeinbornLanguage Model Pretraining Multilingual Language Models
University of Göttingen, Germany
Turnslide: Scalable Multi-Turn Data Synthesis by Walking a Finite-State Machine
Oct 5, 2026Aaron Fainman, Gabriela Kadlecová, Maciej Gryka +7LLM Fine-Tuning Synthetic Data Generation
Imperial College London · distil labs · Agon +1
Learning to Simulate Individuals from Macro Social Signals
Oct 5, 2026Yining Zhao, Bushi Liu, Haofei Yu +5Human Behavior Simulation RL for Language Model Reasoning
University of Illinois Urbana-Champaign · Independent Researcher
CodeForge-MA: Execution-Verified Multi-Agent Learning with Language-Conditioned LoRA for Multilingual Code Generation
Oct 4, 2026Zhizhou Gu, Xianting Wu, Siyu Gu +2RL for Code Generation Synthetic Data Generation for Language Models
Northeastern University Bellevue, USA · Columbia University New York, USA · University of Pennsylvania Philadelphia, USA +2
GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets
Oct 2, 2026Haoyue Yang, Jingyao Li, Zhengfan Wu +3Agentic Code Generation Synthetic Data Generation for Language Models
Institute of Automation, Chinese Academy of Sciences · Baidu Inc., Beijing, China
Recursive Harness Self-Improvement for Frontier Reasoning Data Synthesis
Oct 2, 2026Wenlong Zhang, Zhengbo Jiao, Chenxu Zhang +5Synthetic Data Generation for Language Models Recursive Self-Improvement
EPIC Lab, Shanghai Jiao Tong University · University of Science and Technology of China · Shanghai University of Finance and Economics +5

Oct 29, 2026J/K move · Enter open · S save

MemSteer: Turning User Histories into Supervision for Agent Memory Optimization

Jie Huang, Cuiling Lan, Qize Yu, Xiulian Peng+1

Peking University · Microsoft Research Asia

LLM Agent Memory Agent Memory Management Synthetic Data Generation for Language Models

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An effective agent memory system should adapt to a user's history and task requirements. Human-annotated supervision can guide this adaptation by providing feedback for evaluating and improving memory designs. Yet suitable labels are rarely available for new user histories, and manually constructing QA pairs and supporting evidence is costly. We introduce MemSteer, which turns unlabeled user histories into question-answer (QA) supervision with supporting evidence for memory-design optimization. MemSteer constructs an evidence-grounded answer plan that specifies how facts across related records jointly support a reference answer. The plan guides question generation, while source-grounded auditing and blind answer recovery check the resulting annotations. To study the utility of this supervision, we develop MemSteer-Opt, which combines QA-based design evaluation with evidence-guided failure diagnosis to revise memory store, retrieval, and context construction without updating model parameters. Experiments on ATM-Bench and RHELM show that this supervision is competitive with official-QA-guided optimization. Relative to Base-BM25, MemSteer-Opt improves ATM-Bench Question Score by 13.4 points and RHELM accuracy by 4.1 and 6.6 points without and with external data, respectively. The supervision also benefits two existing optimizers on ATM-Bench, while new-user experiments demonstrate continued design optimization using supervision generated from the target histories.