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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Geometric Calibration of Supervised Fine-Tuning
Oct 13, 2026Jianzhu Bao, Jia-Chen Gu, Qingyuan Liu +4LLM Fine-Tuning Supervised Fine-Tuning
Nanyang Technological University · University of California, Los Angeles
Collapse, Repair, and Drift: Dissecting Fine Tuning Dynamics in Speech Language Models
Oct 13, 2026Yichi Wang, Wangjin Zhou, Yizhou Zhang +4Supervised Fine-Tuning Neural Network Training Dynamics
Department of Intelligence Science and Technology, Kyoto University · Xiaowei, WXG, Tencent · Communication University of China
TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training
Oct 13, 2026Yuqian Fu, Kaiwen Jiang, Zijun Song +3Continual Learning for LLM Agents LLM Agent Training
SKL-MAIS, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · University of Science and Technology of China
Soft Teacher Forcing
Oct 12, 2026Mohammad Fahes, Cordelia Schmid, Anurag ArnabSupervised Fine-Tuning LLM Reasoning
Google Deepmind
SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise
Oct 12, 2026Xupeng Chen, Chenqian Le, Jiayi Cheng +2Language Model Post-Training LLM Training
New York University · University of Southern California · Columbia University
Vela: User Behavior as Supervision Sparks LLM Intelligence
Oct 10, 2026Yang Xiao, Yusong Sun, Haoyi Wu +17LLM Training LLM Post-Training
Quality, Not Just Outcome: A Structural Scoring Framework for Selecting Coding-Agent Trajectories for Supervised Fine-Tuning
Oct 9, 2026Sida Li, Yuanxin Liu, Shicheng Li +4AI Coding Agents Training Data Selection
National Key Laboratory for Multimedia Information Processing, Peking University · Huawei Technologies Co., Ltd.
What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training
Oct 9, 2026Yuxuan Gu, Kequan Niu, Xiaocheng Feng +3Supervised Fine-Tuning Language Model Post-Training
Harbin Institute of Technology
Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution
Oct 8, 2026Haohui Wang, Jiahao Xu, Wangzhi Zhan +9Supervised Fine-Tuning Instruction-Tuning Data Selection
Virginia Tech · Amazon · Meta +2
SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning
Oct 8, 2026Kenan Tang, Andong Hua, Chengxuan Qian +2LLM Fine-Tuning Catastrophic Forgetting
University of California, Santa Barbara
Successive Training Stages and Large Language Model Persuasion: Effects of Misalignment, Supervised Fine-Tuning, and Preference Optimization
Oct 7, 2026Antony Dalmiere, Pascal Marchand, Guillaume Auriol +1Persuasion in Language Models LLM Post-Training
LAAS-TRUST · LAAS-TRUST, INSA Toulouse · LAAS-TSF, LAAS
Training Language Models To Be Coherent Decision-Makers
Oct 6, 2026Khurram Yamin, Xavier Fernandes, Paul Koch +2LLM Decision-Making Decision-Making under Uncertainty
Carnegie Mellon University · Microsoft
Selecting Long-Horizon Trajectories for Reliable and Efficient Terminal-Agent Training
Oct 5, 2026Cuong Dang, Hoang Anh Just, Ruoxi JiaSupervised Fine-Tuning Trajectory Generation
Department of Electrical and Computer Engineering Virginia Tech Blacksburg, VA, USA
Beyond Refusal Patterns: Safe-Role Internalization for Robust and Generalizable LLM Safety Alignment
Oct 4, 2026Jinghao Pang, Jitai Hao, Qiang Huang +2LLM Alignment LLM Safety Alignment
Harbin Institute of Technology (Shenzhen) · Leiden University
Learning without Overwriting: A Theory of Self-Distillation and Supervised Fine-Tuning in Continual Reasoning
Oct 4, 2026Shinichi Uemura, Taiji SuzukiSupervised Fine-Tuning Continual Learning for LLMs
Department of Mathematics Informatics, The University of Tokyo · Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
Learning Style, Forgetting Semantics: A Case Study of SFT and RFT on Classification Tasks
Oct 1, 2026Haodong Liang, Yanhao Jin, Krishnakumar Balasubramanian +1Supervised Fine-Tuning RL Fine-Tuning
University of California, Davis
Keyword Harnesses Fail Open: A Cheap Diagnostic Ladder for Tool-Use Claims in Small Language Models
Oct 1, 2026Juan S. SantillanaSupervised Fine-Tuning Tool-Use Evaluation
Independent Researcher
Finetuning with Sampling: SFT Learns Better Than You Think
Oct 1, 2026Aayush Karan, Sitan Chen, Yilun DuSupervised Fine-Tuning Adaptive Sampling
Harvard University
PG-SFT: Balancing Capability Acquisition and Retention in Offline Agent Fine-Tuning
Oct 1, 2026Ronghua Li, Zi Liang, Zhishan Li +1Supervised Fine-Tuning LLM Agent Training
The University of Hong Kong · The Hong Kong Polytechnic University · Independent Researcher
Comparison of techniques for fine-tuning open-weight models for entity extraction from radiology reports
Sep 30, 2026Aawez Mansuri, Kush Mehta, Mohammadreza Chavoshi +10Supervised Fine-Tuning Synthetic Data Generation
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA · Department of Computer Science, Emory University, Atlanta, GA, USA
SkillGym: Training Skill-Use Agents with Automatic Verifiable Environment Generation
Sep 29, 2026Renxi Wang, Mingshan Hee, Fajri Koto +2Supervised Fine-Tuning LLM Agent Skill Learning
Mohamed bin Zayed University of Artificial Intelligence
On-Policy Parameter Update Direction Underlies Generalization in LLM Post-Training
Sep 29, 2026Shufan Shen, Zhongni Hou, Junshu Sun +5LLM Post-Training Fine-Tuning
State Key Lab. of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy Sciences · Meituan
From Checkpoint Variation to Selection Gains in Supervised Fine-Tuning
Sep 29, 2026Yupeng Chang, Wenxuan Zhang, Yuan WuModel Selection Supervised Fine-Tuning
School of Artificial Intelligence, Jilin University · Singapore University of Technology and Design · Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University
ROSS: Relearning from Self-Generated Rollouts through Selective Supervision
Sep 28, 2026Zhiwei Zhang, Huayu Deng, Fei Zhao +4Supervised Fine-Tuning Self-Training for Language Models
AllSpark Team
The Privacy Fallacy of Crowdsourced Fine-Tuning: Extracting Proprietary Data via Topic-Based Poisoning
Sep 27, 2026Sae Furukawa, Alina OpreaSupervised Fine-Tuning Privacy Leakage in Language Models
Khoury College of Computer Sciences, Northeastern University
Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
Sep 27, 2026Cunchun Li, Haonan He, Yifan Gao +4Supervised Fine-Tuning Adaptive Loss Weighting
Shanghai AI Laboratory · University of Science and Technology of China · Fudan University +2
SoFT: Soft Targets for Generalizable LLM Fine-Tuning
Sep 26, 2026Huihao Jing, Wenbin Hu, Shaojin Chen +7LLM Fine-Tuning Fine-Tuning
HKUST, HKUST-GZ
Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging
Sep 26, 2026Jingyuan Huang, Zuming Huang, Yucheng Shi +4Language Model Merging Language Model Distillation
University of Georgia · INF Tech · Tencent +1
Understanding the Synergy between SFT, RLVR, and OPD in LLM Post-Training
Sep 25, 2026Emre Can Acikgoz, Yang Li, Zeyu Leo Liu +4LLM Post-Training Language Model Distillation
Salesforce AI Research · UIUC
RoboMonitor: Label-Efficient Runtime Monitoring of Robot Task Execution via Predictive Representation Learning
Sep 25, 2026Abhiroop Ajith, Gokul Narayanan, Kyle Coelho +7Robotics VLMs for Robotics
Siemens Corporation, USA · ELPIS Lab, Worcester Polytechnic Institute, USA

Oct 13, 2026J/K move · Enter open · S save

Geometric Calibration of Supervised Fine-Tuning

Jianzhu Bao, Jia-Chen Gu, Qingyuan Liu, Haozhen Zhang+3

Nanyang Technological University · University of California, Los Angeles

LLM Fine-Tuning Supervised Fine-Tuning OOD Generalization

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Recent studies investigate the weight geometry through its spectral structure, which characterizes how the weights transform different input directions. Within this view, supervised fine-tuning (SFT) is shown to induce dense updates that perturb the spectral geometry of pre-SFT weights along their principal directions, potentially disrupting the model's prior knowledge. These observations motivate using the geometry of pre-SFT weights as a reference for refining learned SFT updates. We study this perspective as Geometric Calibration and develop SPACE Subspace Projection for Adapted Checkpoint Enhancement), a lightweight post-hoc method that selectively reverts SFT update components within the principal subspace of the pre-SFT weights while preserving those in its orthogonal complement. SPACE is a data-free, training-free method that requires only the pre- and post-SFT checkpoints. We evaluate SPACE on Qwen2.5, Qwen3, Qwen3.5, Llama-3.1, and Olmo-3 models ranging from 2B to 14B, across mathematical, coding, and medical SFT, as well as several SFT variants. Overall, SPACE mitigates out-of-domain (OOD) capability regression while further improving in-domain (ID) performance over the uncalibrated SFT checkpoints. For math SFT, SPACE yields average ID/OOD gains of 2.85/2.26 points on Qwen2.5-7B, and 2.41/1.52 points on Qwen3-14B-Base, respectively. These results highlight how the geometric relationship between SFT updates and pre-SFT weights shapes the adapted model's capabilities and, more broadly, motivate geometry-aware parameter updates for continual model improvement.