Supervised Fine-Tuning

Also known as SFT

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Latest papers 208

29 papers in the last four weeks, up 12% on the four weeks before. 0.2% of all new papers.

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Self-Specialized Teachers for Domain Post-Training
Aug 16, 2026Yifei Li, Rongman Xu, Lingling Zhang +5Domain Adaptation for LLMs Catastrophic Forgetting
School of Computer Science and Technology, Xi’an Jiaotong University · MOE KLNN Lab, Xi’an Jiaotong University
Capacity-Dependent Effects of Data Selection for Reasoning
Aug 13, 2026Cuong Dang, Hoang Anh Just, Ruoxi JiaInstruction-Tuning Data Selection Supervised Fine-Tuning
Department of Electrical and Computer Engineering Virginia Tech Blacksburg, VA, USA
Reasoning for Social Audio-Visual Question Answering: Where Do We Stand?
Aug 13, 2026Koen P. de Vries, Xavier Alameda-Pineda, Estefanía Talavera +1Supervised Fine-Tuning Multimodal Large Language Models
Inria at Univ. Grenoble Alpes, CNRS, LJK, France · University of Twente, The Netherlands
When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
Aug 12, 2026Siddharth Chauhan, Thomas Butler, Abhishek Singhania +2Supervised Fine-Tuning Multilingual Language Model Evaluation
Amazon.
Weightless Fine-Tuning: Personalizing LLMs via Logit-Space Transport
Aug 11, 2026Bohan Zhang, Anqi Ni, Yixin Wang +1Supervised Fine-Tuning LLM Personalization
University of Michigan · University of Chicago
Adaptive Supervised Anchoring for On-Policy Self-Distillation
Aug 8, 2026Meilin Yang, Zixuan Ding, Jianhao Nie +5Supervised Fine-Tuning On-Policy Self-Distillation
Renmin University of China, Beijing, China · Renmin University of China, Beijing, China.
Reasoning Core: Designing Broad Procedural Data for Completion-Supervised Reasoning Training
Aug 5, 2026Damien Sileo, Valentin Lacombe, Dimitri KachlerSupervised Fine-Tuning Synthetic Data Generation for Language Models
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStal, F-59000 Lille, France
Beyond Full-Model Rollback: AuroSFT for Adapter-State Multi-Task Fine-Tuning
Aug 5, 2026Yue Han, Ziniu LiuSupervised Fine-Tuning Multi-Task Learning
College of Systems Engineering, National University of Defense Technology · College of Computer Science and Technology, National University of Defense Technology
Guideline-as-Oracle: Zero-Annotation Training of an Ophthalmic Telephone Triage Agent
Aug 5, 2026Chenyu Wang, Yi Liu, Baoqing Li +2Supervised Fine-Tuning Clinical Triage
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences · Shanghai Artificial Intelligence Laboratory · ByteDance Inc.
SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs
Aug 4, 2026Kejian Zhu, Zhuoran Jin, Shangqing Tu +5Supervised Fine-Tuning Gradient Interference
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Tsinghua University, Beijing, China
Wnuan: Staged Post-Training for Question Answering over Proprietary Enterprise Knowledge
Aug 3, 2026Xiaofeng Shi, Xiaosong Qiu, Wenxin Ma +6Supervised Fine-Tuning Knowledge Augmentation for Language Models
Beijing Academy of Artificial Intelligence (BAAI) · Beijing District Heating Group Co., Ltd. (BDHG) · Beijing University of Posts and Telecommunications (BUPT)
CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning
Jul 31, 2026Yuxin Chen, Hari Srikanth, Nathan Jew +7Supervised Fine-Tuning Fine-Tuning
University of California, Berkeley · Google DeepMind · NVIDIA Research
Objective-Aligned Direct Answer SFT for Robust Multi-Frame Medical VQA
Jul 30, 2026Site Li, Jianyi Hao, Xiaofeng LiuSupervised Fine-Tuning VLM Robustness
Yale University
Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models
Jul 28, 2026Anton de la Fuente, Arthur ConmySupervised Fine-Tuning LLM Alignment
DomainPilot: Domain-Level Loss-Guided Two-Stage Data Mixture Optimization for Efficient Language Model Fine-Tuning
Jul 24, 2026He ZhangSupervised Fine-Tuning Large Language Model-Guided Optimization
Tsinghua University
SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement
Jul 21, 2026Arther Tian, Alex Ding, Simon Wu +1Supervised Fine-Tuning LLM-as-a-Judge
DGrid AI
A Dual-Hypothesis Reasoning Framework for LLM Guardrails
Jul 20, 2026Md Asiful Islam, Fahmida Alam, Mihai SurdeanuSupervised Fine-Tuning Language Model Safety Evaluation
Department of Computer Science University of Arizona, USA
A Systematic Evaluation of Trajectory Data Curation for LoRA Fine-Tuning of Code Agents
Jul 19, 2026Yunze HanSupervised Fine-Tuning LLM Evaluation
Vdynamics GmbH, 80935 Munich, Germany
Supervised Fine-Tuning vs. In-Context Learning: An Equilibrium Analysis of LLM Personalization under Congestion
Jul 15, 2026Fengzhuo Zhang, Zhuoran Yang, Dirk BergemannSupervised Fine-Tuning LLM Serving
Yale University, 30 Hillhouse Ave., New Haven, CT 06520, USA
Rethinking Speech Foundation Model Fine-tuning: Better SFT or Better Match?
Jul 15, 2026Wangjin Zhou, Yizhou Zhang, Yichi Wang +1Supervised Fine-Tuning Fine-Tuning
Graduate School of Informatics, Kyoto University, Kyoto, Japan
Self-Supervised Visual Representation Learning: Pretrain-Finetuning or Joint Training?
Jul 14, 2026Nusrat Munia, Tyler Ward, Nishat Nayla +2Supervised Fine-Tuning Semi-Supervised Learning
Department of Computer Science University of Kentucky Lexington, KY 40506, USA · Kentucky Geological Survey University of Kentucky Lexington, KY 40506, USA
Online Data Selection Is Implicit Alignment
Jul 8, 2026Aoxiong Zeng, Yuxin Yang, Xiangquan YangSupervised Fine-Tuning LLM Alignment
East China Normal University · Shanghai University
LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure
Jul 6, 2026Yueyang Wang, Baolong Bi, Shuo Lu +1Supervised Fine-Tuning Fine-Tuning
School of Mathematical Sciences, Peking University · Institute of Computing Technology, Chinese Academy of Sciences · Institute of Automation, Chinese Academy of Sciences +1
A Few Teacher Steps Go a Long Way: Cost-Efficient On-Policy Data Augmentation for Agent Post-Training
Jul 6, 2026Junze Ye, Jiayi Cheng, Miao Lu +3Supervised Fine-Tuning Data Augmentation
Stanford University · New York University
NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation
Jul 5, 2026Junjie Tang, Jun Huan, Hao Zhou +2Supervised Fine-Tuning AI Coding Agents
Amazon Web Services
Defending Against Harmful Supervision Hidden in Benign Samples
Jun 29, 2026Bang An, Yibo Yang, Dandan Guo +3Supervised Fine-Tuning Fine-Tuning
1King Abdullah University of Science and Technology · School of Artificial Intelligence, Jilin University
Do We Still Need Fine Tuning? Turkish Sentiment Analysis in the Era of Large Language Model
Jun 28, 2026Sercan Karakaş, Yusuf ŞimşekSupervised Fine-Tuning LLM Evaluation
University of Chicago · Fırat University
Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Jun 27, 2026Young-Jun Lee, Seungone Kim, Minki Kang +5Supervised Fine-Tuning Evolutionary Optimization
University of Minnesota · Carnegie Mellon University · KAIST +3
TRACE: Trajectory-Based Safety Patch Learning for LLM Post-Training Realignment
Jun 26, 2026Changyue Li, Jiaming He, Youliang Yuan +4Supervised Fine-Tuning LLM Safety Alignment
The Chinese University of Hong Kong, Shenzhen · Ant Group · Lero the Research Ireland Centre for Software, University of Limerick
NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research
Jun 25, 2026Qiaobo Hao, Yangqian Wu, Shunyi Wang +5Supervised Fine-Tuning RL for Language Models
ZTE NebulaL0 Post-Training Team

Aug 16, 2026J/K move · Enter open · S save

Self-Specialized Teachers for Domain Post-Training

Yifei Li, Rongman Xu, Lingling Zhang, Muye Huang+4

School of Computer Science and Technology, Xi’an Jiaotong University · MOE KLNN Lab, Xi’an Jiaotong University

Domain Adaptation for LLMs Catastrophic Forgetting Knowledge Distillation Supervised Fine-Tuning

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Target-only post-training can improve performance in a specialized domain while degrading behaviors that a general-purpose base model acquired before adaptation. We study this problem when target-domain data are available but a representative replay corpus is not. We propose self-specialized teacher distillation (SSTD), a two-stage procedure that first trains a copy of the base model into a domain teacher, then distills its token distribution to a student on prefixes sampled from the student itself. Teacher training combines standard target supervision with base-aware key-token weighting and distribution alignment to the frozen base model; on-policy distillation then places domain feedback on states the student can encounter at inference time. On financial numerical reasoning, medical question answering, and legal holding identification, SSTD retains much of the target improvement of direct fine-tuning while improving the mean score on the evaluated general suite by 4.8--5.0 points at the reported operating point. The pattern persists across Qwen3 sizes and on Gemma backbones. SSTD requires neither an external teacher nor general replay data.