Training Data Selection

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16 papers in the last four weeks, up 220% on the four weeks before. 0.2% of all new papers.

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CoTrace: Data Recipes for Training Terminal Agents with Harness-Model Co-Evolution
Oct 7, 2026Jixuan Chen, Jiaxin Zhang, Qinyuan Ye +11Terminal Agents LLM Agent Harnesses
University of California, San Diego · Salesforce AI Research · University of Washington
Dataset Pruning from First Principles: A Label-Free Linear Programming Approach
Oct 7, 2026Rodrigo Schuller, Francisco GanacimDataset Pruning Training Data Selection
Instituto de Matem´atica Pura e Aplicada (IMPA) Rio de Janeiro, Brazil
A Fine-Grained Analysis of the LoRA Fine-Tuning Landscape with Implications for Data Selection
Oct 5, 2026Bowen Zhang, Changrui Fang, Xinsong Ma +2Low-Rank Adaptation Training Data Selection
City University of Hong Kong · Shanghai University of Finance and Economics
Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)
Oct 1, 2026Zilin Du, Bowen Yang, Boyang Albert LiTraining Data Selection
College of Computing and Data Science Nanyang Technological University Singapore
Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning
Sep 30, 2026Muzaffer Citir, Hiroki Nishikawa, Sangyoung ParkClient Selection in Federated Learning Non-IID Federated Learning
Smart Mobility Systems, Technical University of Berlin, Germany · Graduate School of Information Science and Technology, The University of Osaka, Japan
The Unequal Influence of Bad Advice: Using Training Data Attribution to Modulate Emergent Misalignment
Sep 29, 2026Gonçalo Paulo, Louis Jaburi, Nora Belrose +2LLM Alignment LLM Fine-Tuning
EleutherAI
Beyond Prompt Count: How Data Shapes Transfer in On-Policy Distillation
Sep 29, 2026Jiaxuan Wang, Jiafei Lyu, Yuchen Cai +8Training Data Selection Language Model Distillation
State Key Laboratory of Novel Software Technology, Nanjing University · School of Intelligence Science and Technology, Nanjing University · LLM Department, Tencent +1
Loss-Guided Pretraining Data Selection for Time-Series Foundation Models
Sep 29, 2026Yike Li, Shaoxu Song, Jianmin WangTime Series Forecasting Training Data Selection
Tsinghua University
From internal representations to model improvement through prediction errors
Sep 28, 2026Yushi Nakaya, Kenichi Higuchi, Shuichi IshidaActive Learning Training Data Selection
Adansons Corp., 214 Homer Ave, Palo Alto, 94301, CA, USA. · Tohoku University, 6-6-11-803 Aza-Aoba Aramaki, Aoba-ku, Sendai, 980-8579, Miyagi, Japan.
When Less Data Favors Smaller Teachers: Rethinking Teacher Capacity and Data Selection for Knowledge Distillation
Sep 28, 2026Minjae Park, Taesun Yeom, Jaeho LeeSelective Knowledge Distillation Training Data Selection
Pohang University of Science and Technology (POSTECH)
How code helps different tasks? A decompositional lens on LLM post-training
Sep 27, 2026Zheng Yu, Yiwei Li, Yishen Chen +4LLM Fine-Tuning Instruction-Tuning Data Selection
Sun Yat-sen University · Shenzhen Loop Area Institute · The Chinese University of Hong Kong, Shenzhen +1
Selecting Diverse SFT Traces Improves Post-RL Generalization
Sep 27, 2026Dylan Zhang, Mingyuan Wu, Jinning LiReasoning Diversity RL for Language Model Reasoning
University of Illinois Urbana-Champaign, work done at Google · Google
Frame-to-Panorama Localization and Context-Aware Sampling for Scene-Specific Ship Detection in a Smart Marina Testbed
Sep 24, 2026Ignat Romanov, Andreas Hadjipieris, Neofytos DimitriouTraining Data Selection Video Frame Selection
Department of Computer Science University of Nicosia Nicosia, Cyprus · Maritime Digitalization Centre Cyprus Marine and Maritime Institute Larnaca, Cyprus
Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics
Sep 23, 2026Zheyu Zhu, Junchao Zhu, Fengbei Liu +7Spatial Transcriptomics Active Learning
University of Pennsylvania, Philadelphia, PA 19104, USA · Vanderbilt University, Nashville, TN, USA · Cornell Tech, New York, NY 10044, USA +4
CRISP: Scalable Importance-Stratified Coresets for Imbalanced Tabular Learning
Sep 22, 2026Hardhik Mohanty, Indrayana Rustandi, Mohamadreza SheibaniClass-Imbalanced Learning Training Data Selection
University of Southern California Los Angeles, California, USA · Coinbase San Francisco, California, USA
Marginal Log-Likelihood Increments under Dirichlet-Smoothed Markov Estimation
Sep 22, 2026Levin David SchwabMarkov Models Process Mining
Are Coreset Selection Methods Worth Their Cost?
Sep 19, 2026Yangze Liu, Zhongyi HanBenchmark Design Training Data Selection
Shandong University
AutoData: Agentic Search for Pre-training Data Selection
Sep 17, 2026Yan Meng, Dhruv Srikanth, Bingchen Zhao +2Language Model Pretraining Automated Algorithm Discovery
University of Amsterdam · Weco AI
RoboDrop: Curating VLA Post-Training Data via Local Gradient Compatibility
Sep 9, 2026Runze Xu, Yuanfan Xu, Cuijie Xu +4Robotic Data Collection Training Data Selection
Tsinghua University · Striding AI
Exact Recovery Thresholds for Weighted Data Selection in Vector-Valued Linear Regression
Aug 31, 2026Guangjian ZhangData Selection Training Data Selection
DICS: Exploring Data Intrinsic Consistency for Visual Instruction Selection
Aug 31, 2026Yuyang Hong, Jinhui Guo, Jiaqi Gu +6Instruction-Tuning Data Selection Training Data Selection
School of Artificial Intelligence, University of Chinese Academy of Sciences · 2MAIS, Institute of Automation, Chinese Academy of Sciences · 3Alibaba Token Hub, Alibaba Group
Token Distribution versus Data Volume: Domain Balancing in Multi-Domain Meeting Summarisation
Aug 16, 2026Ashima Sood, Bryan Gardiner, Joan CondellLLM Fine-Tuning Text Summarization
School of Computing, Engineering and Intelligent Systems, Ulster University Londonderry, Northern Ireland, United Kingdom
Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent
Aug 11, 2026Fanqi Zhou, Qiaosheng Chen, Zixian Huang +1Training Data Curation Instruction-Tuning Data Selection
State Key Laboratory of Novel Software Technology, Nanjing University · Shanghai Artificial Intelligence Laboratory
Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks
Aug 10, 2026Yi Yang, Xiaoke Chen, Jinyang Huang +6Backdoor Attacks Data Selection
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China · School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China · John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China +1
TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity
Aug 8, 2026Yen-Ku Liu, Hongjie Chen, Ryan A. Rossi +1Training Data Selection
National Yang Ming Chiao Tung University Hsinchu, Taiwan · Dolby Laboratories Atlanta, Georgia, USA · Adobe Research San Jose, California, USA +1
Perturbation Sensitivity at Convergence: A Simple Signal for Identifying Spuriously Correlated Samples
Aug 5, 2026Nilesh KumarTraining Data Selection Classification
One Knob to Rule Them All: A Unified Optimal Transport View of Cold-Start Active Learning
Aug 4, 2026Ning Zhu, Xiaochuan Ma, Juntao Xu +4Active Learning Training Data Selection
Glasgow College, University of Electronic Science and Technology of China · School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China · School of Mathematical Sciences, University of Electronic Science and Technology of China
GLOBE: Trajectory-Aligned Gradient Matching with Structured SparseOptimization for Coreset Selection
Aug 3, 2026Hetian Liu, Jin Cui, Mengcheng Shi +4Training Data Selection Coreset Selection
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University · School of Software Engineering, Xi’an Jiaotong University · School of Computer Science and Technology, Xi’an Jiaotong University
RTLCurator: Label-Efficient Data Curation for RTL Generation
Jul 31, 2026Siyang Cai, Cangyuan Li, Wenjing Chang +4Training Data Curation Training Data Selection
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China · CICS, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China · Computer Network Information Center, Chinese Academy of Sciences, Beijing, China +1
Understanding Context Sampling in TabPFN on Small Tabular Datasets
Jul 29, 2026Mohammed AbdullahTabular Foundation Models Tabular Classification
Department of AI & Data Science Anna University

Oct 7, 2026, cs.CLJ/K move · Enter open · S save

CoTrace: Data Recipes for Training Terminal Agents with Harness-Model Co-Evolution

Jixuan Chen, Jiaxin Zhang, Qinyuan Ye, Yada Pruksachatkun+10

University of California, San Diego · Salesforce AI Research · University of Washington

Terminal Agents LLM Agent Harnesses Agent Harness Optimization Training Data Selection

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Terminal-agent capability depends jointly on model weights and the runtime harness that formats prompts, binds tools, and handles error recovery. Existing harness-model co-evolution approaches improve both components, yet often treat trajectories produced during harness search as an undifferentiated replay buffer. This practice overlooks that a trajectory's value for model training depends on the harness under which it was generated. To systematically analyze this interface, we establish an alternating co-evolution framework that decouples harness search and policy training through component-wise promotion decisions. Within this framework, we introduce CoTrace, a harness-aware data recipe that explicitly governs trajectory routing, provenance matching, and curriculum refresh. Under CoTrace, recurring execution failures guide harness synthesis, while policy training is strictly conditioned on verified rollouts matched to the adopted runtime for supervised fine-tuning (SFT) or fresh online interactions for reinforcement learning (RL). On the Tmax promotion split, CoTrace advances Qwen3.5-9B from 78 to 88 solved tasks under supervised fine-tuning while an online reinforcement variant reaches 90. Specifically, a compact harness-matched corpus produces steady model gains at substantially lower compute than much larger corpora pooled across sibling harnesses. Furthermore, evaluations on Terminal-Bench 2.1 and SWE-bench Lite show that out-of-distribution transfer depends fundamentally on harness compatibility, where maintaining consistency between training and evaluation runtimes prevents procedural execution breakdowns observed under foreign scaffolds.

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