Early Stopping

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5 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping
Oct 9, 2026Danil Rudenko, Georgii Bulgakov, Nikolay Kartashev +3Tabular Regression Quantile Regression
Yandex Research · BRAInLab · HSE University
When Is Enough Enough in Self-Evolving LLM Systems?
Oct 3, 2026Enoch Yin, Bin Liu, Zhengling QiEarly Stopping Language Model Self-Improvement
Department of Statistics, The George Washington University · Department of Statistics and Data Science, School of Management, Fudan University · Department of Decision Sciences, School of Business, The George Washington University
The Model Knows When to Stop: Training-Free Early Stopping for Long-Context Reading
Sep 28, 2026Muath Alyobi, Mohamed Eltahir, Almoayyad Abuljdail +3Early Stopping Long-Context Language Model Evaluation
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia · Department of Computer Science, Edge Hill University, Ormskirk, England
Do We Need Complex Topology Control? Distinct-Peer Random Routing Improves Cost-Efficiency in Sparse Multi-Agent Debate
Sep 22, 2026Boxuan Wang, Zhuoyun Li, Xiaowei Huang +1Multi-Agent Debate LLM Agent Routing
School of Computer Science and Informatics, University of Liverpool, United Kingdom
A Smoothed Discrepancy Principle for Random Feature Methods and Neural Networks
Sep 17, 2026Mike Nguyen, Nicole MückeEarly Stopping Nonparametric Regression
Technical University of Braunschweig
Early-Stopping Thresholds for ES-HyperNEAT: A Data-Driven Approach from Fitness Dynamics
Sep 11, 2026Romain Claret, Arthur Gygax, Michael O'Neill +2Early Stopping Evolutionary Optimization
University of Neuchâtel, Switzerland · Information Management Institute · Computer Science Department +1
Stable Answers, Unfinished Reasoning: Why Self-Consensus Is Not a Safe Early-Exit Signal
Sep 9, 2026Yunxiang Mo, Donghao Zhao, Hejia GengEarly Stopping LLM Reliability
HKUST · University of Oxford
Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Reranking
Sep 1, 2026Guangyu Chen, Boxuan Lyu, Hidetaka Kamigaito +2Early Stopping Machine Translation
Institute of Science Tokyo · Nara Institute of Science and Technology
Adaptive Regularization for Random Features: A Neighboring Early-Stopping Rule with Oracle-Rate Guarantees
Aug 26, 2026Caixing Wang, Zhibo Chen, Yue WangRandom Feature Methods Kernel Ridge Regression
School of Statistics and Data Science, Southeast University · Faculty of Business for Science and Technology, School of Management, University of Science and Technology of China
A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm
Aug 25, 2026Duy Hoang, Bastien Berret, Olivier Bruneau +1Early Stopping Neural Network Generalization
Universit´e Paris-Saclay, CNRS, ENS Paris-Saclay, LMF Gif-sur-Yvette, 91190, France · Universit´e Paris-Saclay, Inria, CIAMS Gif-sur-Yvette, 91190, France · Universit´e Paris-Saclay, ENS Paris-Saclay, LURPA Gif-sur-Yvette, 91190, France
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
Aug 6, 2026Boning Li, Yu Chen, Longbo HuangEarly Stopping Imperfect-Information Games
IIIS, Tsinghua University
Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification
Aug 6, 2026Alex Buna, Shirley Xiaoqi Liu, Patrick RebeschiniEarly Stopping Gradient Descent
Department of Statistics, University of Oxford
Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent
Jul 24, 2026Peng ZhaoSpectral Regularization Early Stopping
Department of Applied Economics and Statistics · University of Delaware
Stop Guessing When to Stop Testing: Efficient Model Evaluation with Just Enough Data
Jul 9, 2026Ofir Arviv, Kristjan Greenewald, Yotam Perlitz +3Early Stopping Optimal Stopping
IBM Research
An interpretable Good--Turing restart criterion for k-means++
Jul 9, 2026Renato Cordeiro de AmorimEarly Stopping Clustering
School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Jul 7, 2026Kai Ruan, Zihe Huang, Ziqi Zhou +4LLM Inference Efficiency Early Stopping
Gaoling School of Artificial Intelligence, Renmin University of China · Institute of Computing Technology, Chinese Academy of Sciences · Duke University +3
AdaStop: Cost-Aware Early Stopping for DNN Test Selection
Jul 6, 2026Bonan Shen, Wei-Jung Huang, Xin Liu +2Early Stopping
Independent Researcher
When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models
Jun 29, 2026Zhe Dong, Fang Qin, Manish ShahEarly Stopping Cost-Aware Inference
University of Maine at Presque Isle · Stanford University · Independent Researcher
Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping
Jun 29, 2026Hsun-Yu Kuo, El Mahdi Chayti, Patrik Reizinger +2Early Stopping OOD Generalization
EPFL, Lausanne, Switzerland · Max Planck Institute for Intelligent Systems & ELLIS Institute, Tübingen, Germany
Halt Fast! Early Stopping for Certified Robustness
Jun 26, 2026Andrew C. Cullen, Paul Montague, Benjamin I. P. RubinsteinEfficient Neural Network Inference Early Stopping
University of Melbourne · DST Group, Adelaide
Semantic Early-Stopping for Iterative LLM Agent Loops
Jun 25, 2026Sahil ShrivastavaEarly Stopping LLM Agent Evaluation
Semantic Halting Problem (SHP) Project
The Geometry of Saturation: Effective Rank Predicts When Labels Stop Helping in Few-Shot Classification
Jun 12, 2026Arnav GuptaEarly Stopping Few-Shot Learning
Independent Researcher, Nepal
Quantitative Performance Analysis of Stopping Criteria for CMA-ES
Jun 8, 2026Ryoji TanabeEarly Stopping Evolutionary Optimization
Yokohama National University, Yokohama, Japan
EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms
Jun 2, 2026Guilin Zhang, Chuanyi Sun, Kai Zhao +3Early Stopping Reinforcement Learning
Workday AI Research · The George Washington University, Washington, DC, USA.
ScoreStop: Gradient-based early stopping using functional score tests
Jun 1, 2026Oliver J. Hines, Christian L. HinesGradient-Boosted Decision Trees Early Stopping
Columbia University, NY, USA · Alan Turing Institute, London, UK
ESPO: Early-Stopping Proximal Policy Optimization
May 28, 2026Zihang Li, Rui Zhou, Yingcheng Shi +8Early Stopping Reinforcement Learning
1Tongyi Lab , Alibaba Group · 2Peking University
Gradient-Flow Optimization as Dynamic Random-Effects Inference: Testing and Early Stopping with Applications to Deep Learning
May 27, 2026Minhao Yao, Ruoyu Wang, Xihong Lin +2Early Stopping Neural Network Training Dynamics
Centre for Biomedical Data Science, Duke-NUS Medical School, National University of Singapore · Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA · Institute of Natural Sciences, MOE-LSC, School of Mathematical Sciences, CMA-Shanghai, SJTU-Yale2026 Joint Center of Biostatistics and Data Science, Shanghai Jiao Tong University +1
A Principled Self-Referenced Early Stopping Approach for Deep Image Prior
May 24, 2026Chaoyan Huang, Cheng-Han Huang, Ismail R. Alkhouri +1Early Stopping Image Reconstruction
Department of Computational Mathematics, Science, & Engineering, Michigan State University · Department of Electrical Engineering and Computer Science, University of Michigan · X Computational Physics Division, Los Alamos National Laboratory +2
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
May 17, 2026Dehai Min, Giovanni Vaccarino, Huiyi Chen +3Early Stopping CoT Reasoning
University of Illinois Chicago · Politecnico di Milano · University of Illinois Urbana-Champaign +1
Overtrained, Not Misaligned
May 12, 2026Joel Schreiber, Ariel GoldsteinEarly Stopping LLM Fine-Tuning
Hebrew University of Jerusalem

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Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping

Danil Rudenko, Georgii Bulgakov, Nikolay Kartashev, Ivan Rubachev+2

Yandex Research · BRAInLab · HSE University

Tabular Regression Quantile Regression Heteroscedastic Regression Robust Loss Functions Early Stopping

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While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom. Several lines of research offer alternatives to the standard Mean Squared Error (MSE), but differences in datasets, backbones, and evaluation protocols make their practical benefits difficult to compare. In this work, we address this gap by performing a systematic re-evaluation of regression objectives across a broad collection of established tabular datasets and multiple neural backbones. Our comparison reveals that many proposed objectives provide limited or inconsistent gains over a properly tuned MSE baseline, while quantile loss yields consistent aggregate improvements over MSE across the tested backbones. We then identify a limitation that remains even with this strong objective: regions with different levels of target noise can reach their best predictive performance at different training epochs. Consequently, a single global early-stopping checkpoint can impose a compromise between uncertainty regions, even when the objective can represent heteroskedastic target distributions. Motivated by this limitation, we introduce a heteroskedasticity-aware extension of quantile loss that uses input-dependent label smoothing as a potential remedy and improves over quantile loss on several backbones. Overall, our results provide a modern empirical picture of regression objectives for tabular deep learning and highlight how differences in optimal stopping times across uncertainty regions can limit the effectiveness of global early stopping. The code is available at https://github.com/yandex-research/tabular-regression.