Supervised Fine-Tuning

Also known as SFT

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Latest papers 211

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

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Modeling Atomic Conformational Ensembles of Proteins via Test-Time Supervision of Boltz-2 on Cryo-EM Density Maps
May 11, 2026Jay Shenoy, Miro Astore, Axel Levy +3Supervised Fine-Tuning Protein Structure Prediction
Department of Computer Science Stanford University, Flatiron Institute, & SLAC National Lab Stanford, CA 94305, USA · Center for Computational Biology & Center for Computational Mathematics Flatiron Institute New York, NY 10010, USA · Department of Electrical Engineering Stanford University & SLAC National Lab Stanford, CA 94305, USA +2
Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning
May 10, 2026Ruiying Peng, Mengyu Yang, Jing Lei +3Supervised Fine-Tuning Memorization in Language Models
Tsinghua Shenzhen International Graduate School · Huawei Technologies
Large Language Models for Sequential Decision-Making: Improving In-Context Learning via Supervised Fine-Tuning
May 9, 2026Minmin Zhang, Sina Aghaei, Soroush SaghafianSupervised Fine-Tuning Sequential Decision Making
Harvard University
Preserving Foundational Capabilities in Flow-Matching VLAs through Conservative SFT
May 9, 2026Tianyi Zhang, Shaopeng Zhai, Haoran Zhang +2Supervised Fine-Tuning Continual Learning for VLA Models
Shanghai Artificial Intelligence Laboratory
Rotation-Preserving Supervised Fine-Tuning
May 8, 2026Hangzhan Jin, Tianwei Ni, Lu Li +3Supervised Fine-Tuning Fine-Tuning
1Mila - Quebec AI Institute · 2Polytechnique Montréal · 3Université de Montréal +3
On Distinguishing Capability Elicitation from Capability Creation in Post-Training: A Free-Energy Perspective
May 8, 2026Yuhao Li, Shengchao LiuSupervised Fine-Tuning Free Energy Principle
Department of Computer Science and Engineering The Chinese University of Hong Kong
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
May 8, 2026Shijun Li, Kaiwen Dong, Xiang Gao +1Supervised Fine-Tuning LLM Alignment
The University of Texas at Austin · Intuit AI Research
Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
May 8, 2026Pingbang Hu, Xueshen Liu, Z. Morley Mao +1Supervised Fine-Tuning Training Data Selection
University of Illinois Urbana–Champaign · University of Michigan
Optimizer-Model Consistency: Full Finetuning with the Same Optimizer as Pretraining Forgets Less
May 7, 2026Yuxing Liu, Jianyu Wang, Tong ZhangSupervised Fine-Tuning LLM Fine-Tuning
1UIUC∗ · 2Apple
Crafting Reversible SFT Behaviors in Large Language Models
May 7, 2026Yuping Lin, Pengfei He, Yue Xing +5Supervised Fine-Tuning Language Model Steering
Michigan State University · Hippocratic AI · University of Georgia
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
May 7, 2026Xinrui Chen, Liu Yang, Ou WuSupervised Fine-Tuning LLM Fine-Tuning
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences · College of Intelligence and Computing, Tianjin University
Navigating by Old Maps: The Pitfalls of Static Mechanistic Localization in LLM Post-Training
May 7, 2026Hang Chen, Jiaying Zhu, Hongyang Chen +3Supervised Fine-Tuning Transformer Interpretability
School of Computer Science and Technology Xi’an Jiaotong University · School of Computer Science and Engineering The Chinese University of Hong Kong · Shaanxi Co., Ltd(Xi’an 710077, China) China Mobile Group +1
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
May 6, 2026Dengyang Jiang, Xin Jin, Dongyang Liu +9Supervised Fine-Tuning Diffusion Model Distillation
1The Hong Kong University of Science and Technology · 2Z-Image Team, Alibaba Group · 4The Chinese University of Hong Kong +1
Stabilizing LLM Supervised Fine-Tuning via Explicit Distributional Control
May 6, 2026Xinyu Wang, Changzhi Sun, Yuanbin Wu +1Supervised Fine-Tuning Continual Learning for LLMs
School of Computer Science and Technology, East China Normal University, Shanghai, China · TeleAI, Shanghai, China
OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories
May 5, 2026Yuwen Du, Rui Ye, Shuo Tang +4Supervised Fine-Tuning Tool-Augmented Language Model Agents
1Shanghai Jiao Tong University
Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent
May 4, 2026Yao Shu, Chenxing Wei, Hongbin Lin +2Supervised Fine-Tuning Reinforcement Learning
Iterative Finetuning is Mostly Idempotent
May 1, 2026Zephaniah Roe, Jack Sanderson, Dang Nguyen +5Supervised Fine-Tuning LLM Post-Training
University of Chicago
Diversity in Large Language Models under Supervised Fine-Tuning
Apr 30, 2026Roman Klypa, Oleksandr CherednichenkoSupervised Fine-Tuning Catastrophic Forgetting
Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, 38000 Grenoble, France · Department of Mathematics and Mathematical Statistics, Integrated Science Lab, Umeå University, Sweden.
How Fast Should a Model Commit to Supervision? Training Reasoning Models on the Tsallis Loss Continuum
Apr 28, 2026Chu-Cheng Lin, Eugene IeSupervised Fine-Tuning Reinforcement Learning
Google
Distilling Self-Consistency into Verbal Confidence: A Pre-Registered Negative Result and Post-Hoc Rescue on Gemma 3 4B
Apr 27, 2026Jon-Paul CacioliSupervised Fine-Tuning Confidence Estimation in Language Models
Independent Researcher, Melbourne, Australia
Preserving Long-Tailed Expert Information in Mixture-of-Experts Tuning
Apr 24, 2026Haoze He, Xingyuan Ding, Xuan Jiang +5Supervised Fine-Tuning Fine-Tuning
Carnegie Mellon University · MIT · UCSD +1
Parallel-SFT: Improving Zero-Shot Cross-Programming-Language Transfer for Code RL
Apr 22, 2026Zhaofeng Wu, Shiqi Wang, Boya Peng +5Supervised Fine-Tuning RL for Code Generation
Meta Superintelligence Labs · MIT
Compatibility-Aware Dynamic Fine-Tuning for Large Language Models
Apr 22, 2026Yucheng Zhou, Junwei Sheng, Qianning Wang +1Supervised Fine-Tuning Fine-Tuning
SKL-IOTSC, CIS, University of Macau · Auckland University of Technology
When Can LLMs Learn to Reason with Weak Supervision?
Apr 20, 2026Salman Rahman, Jingyan Shen, Anna Mordvina +3Supervised Fine-Tuning RL for Language Model Reasoning
University of California, Los Angeles · New York University · Google
Generalization Boundaries of Fine-Tuned Small Language Models for Graph Structural Inference
Apr 20, 2026Michal PodstawskiSupervised Fine-Tuning Small Language Models
NASK National Research Institute Warsaw, Poland
Post-training is (Massive) Supervised Learning
Apr 20, 2026Michael Hassid, Yossi Adi, Roy SchwartzSupervised Fine-Tuning Language Model Post-Training
1FAIR, Meta AI · 2The Hebrew University of Jerusalem
Bridging the Reasoning Gap in Vietnamese with Small Language Models via Test-Time Scaling
Apr 20, 2026Bui The Trung, Do Minh Duc, Nguyen Van Vinh +1Supervised Fine-Tuning Mathematical Reasoning Benchmarks
Computer Science and Engineering VNU Vietnam Japan University Hanoi, Vietnam · Information Science JAIST Ishikawa, Japan · Faculty of Information VNU University of Engineering and Technology Hanoi, Vietnam +1
REALM: Reliable Expertise-Aware Language Model Fine-Tuning from Noisy Annotations
Apr 19, 2026Sajjad Ghiasvand, Mark Beliaev, Mahnoosh Alizadeh +1Supervised Fine-Tuning LLM Fine-Tuning
Department of Electrical and Computer Engineering UC Santa Barbara Santa Barbara, CA 93106, USA
Why Fine-Tuning Encourages Hallucinations and How to Fix It
Apr 16, 2026Guy Kaplan, Zorik Gekhman, Zhen Zhu +5Supervised Fine-Tuning Continual Learning for LLMs
♡Hebrew University of Jerusalem · ♣Technion – Israel Institute of Technology · ♢University of Illinois Urbana-Champaign +1
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
Apr 15, 2026Zekai Lin, Chao Xue, Di Liang +8Supervised Fine-Tuning Selective Fine-Tuning
Tencent Hunyuan · University of New South Wales · Tencent Yuanbao +2

May 11, 2026J/K move · Enter open · S save

Modeling Atomic Conformational Ensembles of Proteins via Test-Time Supervision of Boltz-2 on Cryo-EM Density Maps

Jay Shenoy, Miro Astore, Axel Levy, Frédéric Poitevin+2

Department of Computer Science Stanford University, Flatiron Institute, & SLAC National Lab Stanford, CA 94305, USA · Center for Computational Biology & Center for Computational Mathematics Flatiron Institute New York, NY 10010, USA · Department of Electrical Engineering Stanford University & SLAC National Lab Stanford, CA 94305, USA+2

Supervised Fine-Tuning Protein Structure Prediction AI-Assisted Scientific Research Cryo-Electron Microscopy

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Knowledge of a protein's atomic conformational ensemble is critical to determining its function, yet state-of-the-art ensemble prediction models are limited by lack of high-quality conformational data from simulation or experiment. Recent advances in heterogeneous reconstruction for cryo-electron microscopy (cryo-EM) have enabled scientists to visualize ensembles of density maps for larger proteins and complexes not typically accessible through simulation, but building atomic models into these maps remains a challenge. Traditionally, ensemble prediction models are trained via a two-stage process: experimental density maps are converted into atomic structural ensembles through model building, after which these structures are used to train sequence-to-atomic ensemble predictors. In this work, we propose a new principle for fine-tuning pre-trained static structure prediction models such as Boltz-2 directly on raw cryo-EM maps, bypassing the two-stage process. We apply this technique to the problem of atomic model building by fine-tuning Boltz-2 to generate atomic conformations from an input ensemble of cryo-EM maps, achieving superior model building accuracy compared to prior work. Beyond overfitting to individual map ensembles, our method, CryoSampler, also shows preliminary evidence of in-domain generalization after fine-tuning, sampling diverse atomic conformations for an unseen sequences within the same protein family without requiring cryo-EM data. These capabilities indicate that CryoSampler holds the potential to train next-generation atomic ensemble prediction models directly on raw cryo-EM measurements.