{"total":28,"rows":[{"id":"2410.07471","title":"SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection","created":"2024-10-09","cited_by_count":64,"area":null},{"id":"2502.04194","title":"The Best Instruction-Tuning Data are Those That Fit","created":"2025-02-06","cited_by_count":52,"area":null},{"id":"2505.17895","title":"DataRater: Meta-Learned Dataset Curation","created":"2025-05-23","cited_by_count":13,"area":null},{"id":"2501.04070","title":"More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives","created":"2025-01-07","cited_by_count":12,"area":null},{"id":"2510.17802","title":"Unbiased Gradient Low-Rank Projection","created":"2025-10-20","cited_by_count":7,"area":null},{"id":"2604.15416","title":"StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models","created":"2026-04-16","cited_by_count":5,"area":null},{"id":"2509.14952","title":"Stochastic Bilevel Optimization with Heavy-Tailed Noise","created":"2025-09-18","cited_by_count":4,"area":null},{"id":"2412.07138","title":"Unlocking TriLevel Learning with Level-Wise Zeroth Order Constraints: Distributed Algorithms and Provable Non-Asymptotic Convergence","created":"2024-12-10","cited_by_count":3,"area":null},{"id":"2510.06048","title":"BLISS: A Lightweight Bilevel Influence Scoring Method for Data Selection in Language Model Pretraining","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2511.22331","title":"On the Condition Number Dependency in Bilevel Optimization","created":"2025-11-27","cited_by_count":3,"area":null},{"id":"2512.04555","title":"ADAPT: Learning Task Mixtures for Budget-Constrained Instruction Tuning","created":"2025-12-04","cited_by_count":3,"area":null},{"id":"2606.04401","title":"TANDEM: Bi-Level Data Mixture Optimization with Twin Networks","created":"2026-06-03","cited_by_count":3,"area":null},{"id":"2409.19800","title":"Differentially Private Bilevel Optimization","created":"2024-09-29","cited_by_count":2,"area":null},{"id":"2510.16806","title":"Computational Budget Should Be Considered in Data Selection","created":"2025-10-19","cited_by_count":2,"area":null},{"id":"2509.02937","title":"Faster Gradient Methods for Highly-Smooth Stochastic Bilevel Optimization","created":"2025-09-03","cited_by_count":1,"area":null},{"id":"2511.09845","title":"Bridging Constraints and Stochasticity: A Fully First-Order Method for Stochastic Bilevel Optimization with Linear Constraints","created":"2025-11-13","cited_by_count":1,"area":null},{"id":"2605.09094","title":"A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization","created":"2026-05-09","cited_by_count":1,"area":null},{"id":"2607.22769","title":"DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning","created":"2026-07-24","cited_by_count":1,"area":null},{"id":"2605.09932","title":"FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.10288","title":"BROS: Bias-Corrected Randomized Subspaces for Memory-Efficient Single-Loop Bilevel Optimization","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.24743","title":"Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning","created":"2026-05-23","cited_by_count":0,"area":null},{"id":"2607.27928","title":"Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2609.11029","title":"Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.30501","title":"To Solve Bilevel Optimization with Nonconvex Lower Levels, We Need Second-Order Stationarity","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2610.01843","title":"Optimal Stochastic Bilevel Optimization with First-Order Oracles","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.02092","title":"Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)","created":"2026-10-01","cited_by_count":0,"area":null},{"id":"2610.16531","title":"Support or Magnitude? Characterizing Neuron Relevance for Instruction Data Selection","created":"2026-10-12","cited_by_count":0,"area":null},{"id":"2610.19844","title":"Memory-Efficient Subspace Optimization with Residual Correction: From Momentum SGD to Muon","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}