Curriculum Learning

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15 papers in the last four weeks, up 150% on the four weeks before. 0.1% of all new papers.

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PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining
Jul 31, 2026Mohammad Sadeq Abolhasani, Viswanath GanapathyRelational Foundation Models Synthetic Data Pretraining
SAP Labs, LLC., Palo Alto 94304, United States.
Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data
Jul 31, 2026Mohammad Sadeq Abolhasani, Viswanath GanapathySynthetic Data Pretraining Synthetic Data Generation
SAP Labs, LLC. Palo Alto, CA, United States
SCOUT: Per-Context Reset Curricula for Sparse-Reward Reinforcement Learning
Jul 29, 2026Siddharth Aphale, Ayushman SinghCurriculum RL Curriculum Learning
Stanford University · Sesame AI
Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning
Jul 28, 2026Yu Su, Nabil AoufLearning from Demonstration Curriculum Learning
School of Science and Technology, City St George’s University of London, London, United Kingdom
Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
Jul 21, 2026Vanessa Toborek, Florian Seiffarth, Sebastian Müller +1Curriculum Learning
University of Bonn, Germany · Lamarr Institute, Germany · Fraunhofer IAIS, Germany
StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design
Jul 20, 2026Yin Wang, Haotian Hu, Jineng Han +4Vision-Language Models VLM Quantization
StepX Team
CASA-SDF: Curriculum-Aware Spatial Adaptation with Curvature-Guided Density for Neural Implicit Surface Reconstruction
Jul 15, 2026Lei Yang, Weiqing Li, Zhiyong Su +1Signed Distance Functions 3D Reconstruction
School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, Jiangsu, China210094 · School of Automation, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing, Jiangsu, China210094
Open-ended Multi-agent Autocurricula via Visual Inspection of Policies with Multi-modal LLMs
Jul 9, 2026Lorenzo Pantè, Andrea Fanti, Roberto CapobiancoGame-Playing Agents Curriculum RL
Sapienza University of Rome, Italy · Sony AI, Zurich, Switzerland
Max Out GRPO Signal: Adaptive Trace Prefix Control for Hard Reasoning Problems
Jul 8, 2026Vladislav BeliaevRL for Language Model Reasoning Group Relative Policy Optimization
Independent Researcher
UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks
Jul 7, 2026Yifan Zhang, Yuxin Hu, Zhuobin Hao +2Curriculum Learning
aCollege of Information Engineering Northwest A&F University Yangling 712100 Shaanxi China bShaanxi Engineering Research Center for Intelligent Perception and Analysis of Agricultural Information Northwest A&F University Shaanxi China · aCollege of Information Engineering Northwest A&F University Yangling 712100 Shaanxi China · cNorthwest A&F University ShenZhen Research Institute Shenzhen 518000 China
Pretraining Curricula Enable Selective Fine-tuning
Jul 6, 2026Sebastian A. Bruijns, Jirko Rubruck, Mia H. Whitefield +3Disentangled Representation Learning Fine-Tuning
University of Oxford · University of Oxford & Martian · Martian
HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum
Jul 2, 2026Chenxin Liu, Qingzhou Lu, Guangxiao Yang +4RL for Robotics Robot Trajectory Tracking
Tsinghua University · RobotEra · Shanghai Qizhi Institute
Curriculum-guided Change Detection Training: Toward Accurate Serac Fall Monitoring
Jun 26, 2026Arthur Dérédel, Carlos Crispim-Junior, Pierre Lemaire +2Remote Sensing Change Detection Curriculum Learning
LIRIS, UMR5205, Universit´e Lumi`ere Lyon 2, CNRS, Ecole Centrale de Lyon, INSA Lyon, Universit´e Claude Bernard Lyon 1, 69676, Bron, France · Styx4D, 19 rue lac Saint Andr´e, Le Bourget-du-Lac, 73370, France
Learning to Reason with Curriculum II: Compositional Generalization
Jun 26, 2026Nived Rajaraman, Audrey Huang, Miroslav Dudik +3Curriculum Learning Compositional Generalization
Microsoft Research. · University of Illinois Urbana-Champaign. · Microsoft Research, New York, NY.
The Geometry of Sequential Learning: Lie-Bracket Prediction of Transfer Order
Jun 23, 2026John SweeneyPairwise Ranking Cross-Domain Transfer Learning
Sideplane AI
Dyna-DINO: Efficient ViT Distillation Via Adaptive Representation Anchoring
Jun 17, 2026Jiaqi Zhang, Ashton Lee, Anthony Wong +3Efficient ViTs Curriculum Learning
Brown University · Rice University
DreamReasoner-8B: Block-Size Curriculum Learning for Diffusion Reasoning Models
Jun 17, 2026Zirui Wu, Lin Zheng, Jiacheng Ye +5Blockwise Diffusion Language Models Efficient Language Model Reasoning
The University of Hong Kong · Peking University
Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effects
Jun 16, 2026Savini Kommalage, Sanka Mohottala, Asiri Gawesha +5Training Data Curation Curriculum Learning
Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka · Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka · Faculty of Engineering, Sri Lanka Institute of Information Technology, Sri Lanka +2
SPICE: Synergy and Partial Information Based Curriculum Evolution
Jun 15, 2026Ankush Pratap Singh, Houwei Cao, Yong LiuCurriculum Learning Multimodal Learning
New York Institute of Technology · New York, NY, USA · New York University
Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning
Jun 10, 2026Enhan Zhao, Wei Wu, Yuanrui Zhang +23D Spatial Reasoning Synthetic Data Generation
1Peking University · 2Ant International · 3The University of Hong Kong
The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring
Jun 9, 2026Ali Keramati, Mark WarschauerFine-Tuning LLM Fine-Tuning
University of California, Irvine
Autonomous Obstacle Removal for Excavators through Policy Learning with Particle Simulation
Jun 8, 2026Yuki Kadokawa, Sandro M. Alcantara Tacora, Taro Abe +4Robot Policy Learning Sim-to-Real Transfer
Nara Institute of Science and Technology · Public Works Research Institute, Ibaraki 300-2621, Japan
Reliable to Expressive: A Curriculum for Rubric-Following Safety Judges
Jun 8, 2026Yongtaek Lim, Hyeji Choi, Minwoo KimLLM-as-a-Judge LLM Safety Evaluation
AI Safety Team, DATUMO.INC, Seoul, South Korea.
INFUSER: Influence-Guided Self-Evolution Improves Reasoning
Jun 8, 2026Siyu Chen, Miao Lu, Beining Wu +7RL for Language Model Reasoning Group Relative Policy Optimization
Yale University · Stanford University · University of Chicago +2
AdaGRPO: A Capability-Aware Adaptive Enhancement for Flow-based GRPO
Jun 5, 2026Jiazi Bu, Pengyang Ling, Yujie Zhou +8T2I Generation Group Relative Policy Optimization
1Shanghai Jiao Tong University · 2S-Lab, Nanyang Technological University · 3Shanghai AI Laboratory +7
Style-CCL: Content-Preserving Style Transfer via Curriculum Continual Learning
Jun 4, 2026Shiwen Zhang, Haoyuan Wang, Xianghao Zang +3Diffusion Transformer Continual Learning
Institute of Artificial Intelligence (TeleAI), China Telecom.
Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation
Jun 3, 2026Ahmed Alansary, Molham Mohamed, Ali HamdiMedical QA Curriculum Learning
Faculty of Computer Science MSA University Giza, Egypt
Representation Curriculum: Stagewise Training for Robust Ranking and Allocation
Jun 3, 2026Ehsan Ebrahimzadeh, Sina Baharlouei, Abraham BagherjeiranCold-Start Recommendation Representation Learning
eBay Search Ranking and Monetization San Jose, California, USA
Learning What to Learn: Stage-Specific Data Sets for SFT-then-RL in Small Language Model Reasoning
Jun 3, 2026Chongyang He, Rui Zhang, Zixuan Wang +1Supervised Fine-Tuning RL for Language Model Reasoning
1Tsinghua University · 2National University of Singapore · 3DiDi +1
Demystifying Data Organization for Enhanced LLM Training
May 28, 2026Yalun Dai, Yangyu Huang, Tongshen Yang +8Language Model Pretraining Training Data Curation
Nanyang Technological University · Microsoft Research · The Hong Kong University of Science and Technology

Jul 31, 2026, cs.LGJ/K move · Enter open · S save

PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining

Mohammad Sadeq Abolhasani, Viswanath Ganapathy

SAP Labs, LLC., Palo Alto 94304, United States.

Relational Foundation Models Synthetic Data Pretraining Relational Reasoning In-Context Learning Curriculum Learning

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Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model training pipeline. We study whether PluRel, a general-purpose synthetic relational database generator, can serve as an external data source for RDB-PFN, a relational in-context learner originally pretrained with a 600K-task single-table warm-up followed by an approximately 1.8M-task adaptation stage. We build a conversion pipeline that maps PluRel-generated databases, including externally constructed binary prediction tasks, into the RDB-PFN training format and evaluate three curriculum strategies: SCHEMA-GUIDED FIRST (real-world schema then fully synthetic), FULLY SYNTHETIC (diverse synthetic schemas throughout), and SCHEMA-GUIDED LAST (fully synthetic then real-world schema). Using only approximately 5,500 relational databases (approximately 33K tasks), roughly 55x fewer tasks than the original protocol, and no single-table warm-up, our best curriculum (SCHEMA-GUIDED FIRST) achieves 0.6346 average ROC-AUC across 19 real benchmark tasks at 1024-shot context, recovering 87.6% of the published RDB-PFN performance (0.7245). At 64-shot context, the gap narrows to 93.8% (0.6116 vs. 0.6517). Our results demonstrate that external synthetic generators can provide useful pretraining signals for RFMs when combined with appropriate curriculum design and that exposure to a real-world schema early in training is substantially more effective than late-stage schema adaptation.

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