Fine-Tuning Language Models

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34 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Behavioral Couplings: From Formation to Behavioral Emergence in Language Models
Oct 16, 2026Dongyoon Hahm, Woogyeol Jin, Dennis Wei +3Emergent Misalignment in Language Models Fine-Tuning Language Models
KAIST · IBM Research
Rank Measures of Fine-Tuning Updates Need a Noise Baseline
Oct 15, 2026Muhammad Usman AltafFine-Tuning Language Models Neural Network Training Dynamics
Independent Researcher
How Low Can You Go: Fine-Tuning LLMs with Bayesian Optimization (LlMBO)
Oct 13, 2026Aditya Kulkarni, Davide Sartor, Carlos Soto +1LLM Fine-Tuning Fine-Tuning Language Models
Department of Physics, University of Massachusetts Amherst · Department of Mathematics and Statistics, University of Massachusetts Amherst
Agaram 2.5B: The First Tamil-First Language Model Trained from Scratch with Tool Calling. A Playbook for Under-Represented Languages
Oct 13, 2026Ramchand Kumaresan, Madhusudhanan G, Kaushik KezendiraneLanguage Modeling Low-Resource Language Processing
Murai Labs · Independent researcher
Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow
Sep 30, 2026Polina Tsvilodub, Andreas Waldis, Linlu Qiu +2Fine-Tuning LLM Fine-Tuning
University of Tübingen · Massachusetts Institute of Technology · New York University
Fine-Tuning LLMs for Translation: General Forgetting Mitigation Does Not Preserve MT-Specific Instruction Following
Sep 23, 2026Niklas Scholz, David Thulke, Abdallah Nasir +3LLM Fine-Tuning Elastic Weight Consolidation
AppTek GmbH, Aachen, Germany · Machine Learning and Human Language Technology, RWTH Aachen University, Germany · Applied Science Private University, Amman, Jordan
North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)
Sep 12, 2026Tom Kocmi, Alexandre Bérard, Phil Blunsom +20Machine Translation Language Model Post-Training
Cohere
Strategy Accumulation and Guided Execution for Automated LLM Fine-Tuning
Sep 6, 2026Haoran Zhao, Wei Du, Dingwen Yang +7Fine-Tuning Language Models LLM Fine-Tuning
Fudan University · Ant Group · Shanghai Jiaotong University
Attention Sensitivity Is Not Enough: Dissociating Attention-Level and Behavioural In-Context Learning under Fine-Tuning
Aug 30, 2026Jinyuan Zhang, Peng He, He Hu +2Fine-Tuning Language Models In-Context Learning
Hubei University, Wuhan, China
Thinking in a Low-Resource Language: What SFT Builds, What RL Fixes, What Accuracy Cannot See
Aug 18, 2026Ayoub Kirouane, Christos PetrocheilosFine-Tuning Language Models Structured Reasoning in Language Models
Sophea AI, KIEFER SA, Athens, Greece
TopoTuner: Topological Finetuning of Large Language Models
Jul 18, 2026Abdulkadir Erol, Yash Mahajan, Vepaul Hariprashad +4Fine-Tuning LLM Fine-Tuning
Department of Computer Science University of Central Florida Orlando, USA · Department of Computer Science University of Manitoba Canada · Department of Finance University of Central Florida Orlando, USA
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning
Jul 10, 2026Ivan Ilin, Philip Zmushko, Peter RichtárikMemory-Efficient Fine-Tuning Low-Rank Adaptation
KAUST
ReCoLoRA: Spectrum-Aware Recursive Consolidation for Continual LLM Fine-Tuning
Jul 4, 2026Wentao LuContinual Learning for LLMs Low-Rank Adaptation
Translating Natural Language to Strategic Temporal Specifications via LLMs
Jun 29, 2026Marco Aruta, Francesco Improta, Vadim Malvone +2Formal Verification Multi-Agent Systems
University of Naples Federico II, Naples, Italy · LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France · STIMicroelectronics, Grenoble, France
The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Jun 27, 2026Will Hawkins, Kaivalya Rawal, Jonathan Rystrøm +8LLM Safety Benchmarks Multilingual Language Model Evaluation
Oxford Internet Institute, University of Oxford · Hasso Plattner Institute, University of Potsdam · Chinese University of Hong Kong +2
Finetuning with Scientific Data Increases Hallucinations: A Multi-domain Factuality Evaluation of LLMs
Jun 19, 2026Raia Abu Ahmad, Nikolas Rauscher, Ekaterina Borisova +3LLM Evaluation LLM Fine-Tuning
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI) · Technische Universität Berlin · Humboldt-Universität zu Berlin
TuneAhead: Predicting Fine-tuning Performance Before Full Training Begins
Jun 16, 2026Yuxiang Luo, Haonan Long, Chen Wang +6Fine-Tuning LLM Fine-Tuning
The Hong Kong University of Science and Technology (Guangzhou) · Huawei Technologies Ltd.
Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study
Jun 11, 2026Dezhi Yu, Yvonne Qiu, ShuoJia FuLLM Alignment Direct Preference Optimization
Fine-Tuning Improves Information Conveyance in Language Models
May 29, 2026Yuwei Cheng, Weiyi Tian, Haifeng XuLanguage Modeling LLM Fine-Tuning
Department of Data Science University of Chicago Chicago, IL 60637 · Department of Computer Science University of Chicago Chicago, IL 60637
Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay
May 25, 2026Martin Marek, Dongkyu Cho, Shikai Qiu +3Continual Learning for LLMs LLM Fine-Tuning
New York University
torchtune: PyTorch native post-training library
May 20, 2026Mark Obozov, Maxime Griot, Joseph Cummings +8LLM Fine-Tuning Efficient Language Model Training
Work done at PyTorch, Meta · Stanford · Meta-FAIR
ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging
May 12, 2026Neha Verma, Nikhil Mehta, Shao-Chuan Wang +7Continual Learning for LLMs LLM Fine-Tuning
Johns Hopkins University · Google DeepMind · Google
Probabilistic Calibration Is a Trainable Capability in Language Models
May 12, 2026Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2LLM Fine-Tuning Controllable Text Generation
Chandar Research Lab · Mila – Quebec AI Institute · LAMA-WeST Lab +3
Locking Pretrained Weights via Deep Low-Rank Residual Distillation
May 11, 2026Keitaro Sakamoto, Pierre Ablin, Federico Danieli +1Open-Weight Language Models LLM Security
Apple
Sparse Memory Finetuning as a Low-Forgetting Alternative to LoRA and Full Finetuning
May 4, 2026Prakhar Gupta, Garv Shah, Satyam Goyal +1Memory-Augmented Language Models LLM Fine-Tuning
University of Michigan
Query2Diagram: Answering Developer Queries with UML Diagrams
Apr 26, 2026Oleg Baryshnikov, Anton M. Alekseev, Sergey I. NikolenkoSoftware Engineering Software Reverse Engineering
HSE University · St. Petersburg Department of Steklov Mathematical Institute, RAS, St. Petersburg State University
When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure
Apr 23, 2026Boyu Xiao, Xiuqi Tian, Xuwen Song +4LLM Sycophancy Healthcare
Research Center for Social Computing and Interactive Robotics, Harbin Institute of Technology, China
Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
Apr 20, 2026Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia +3Continual Learning for LLMs Mixture-of-Experts Language Models
University of Washington · Allen Institute for AI · University of California, Berkeley
MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages
Mar 21, 2026Anri Lombard, Simbarashe Mawere, Temi Aina +5African Language Processing Multilingual Language Models
University of Cape Town, Cape Town, South Africa
How Clued up are LLMs? Evaluating Multi-Step Deductive Reasoning in a Text-Based Game Environment
Mar 17, 2026Rebecca Ansell, Autumn Toney-WailsFine-Tuning Language Models LLM Agent Evaluation
Department of Computer Science, Georgetown University, Washington, DC, USA · Syntheos, Corp; UNU-Merit, Maastricht, NL

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

Behavioral Couplings: From Formation to Behavioral Emergence in Language Models

Dongyoon Hahm, Woogyeol Jin, Dennis Wei, Yi Zhou+2

KAIST · IBM Research

Emergent Misalignment in Language Models Fine-Tuning Language Models

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Fine-tuning large language models (LLMs) can induce behaviors beyond those explicitly targeted. For example, training a model to respond more warmly can make it more sycophantic, and training it to write insecure code can cause broad misalignment on prompts unrelated to coding. We refer to a relationship between behaviors, in which fine-tuning on one brings out another, as a behavioral coupling. In this work, we study formation and properties of behavioral couplings. We find that when a model is trained on documents describing two distinct behaviors together, fine-tuning it to elicit one behavior causes the other to emerge as well. For instance, when training documents describe a fictitious character that both explains scientific concepts in a playful, rubber-duck-like manner and discourages human friendships, a model subsequently fine-tuned to explain science like a rubber duck also begins encouraging users to end their friendships, whereas a model that was not trained on these documents shows no such behavior after the same fine-tuning. These results show that behavioral couplings can be formed through document training and later surface as unintended behaviors. More concerningly, we find that couplings can compose. When two document sets share a behavior (document A pairs behaviors 1 and 2, and document B pairs behaviors 2 and 3), fine-tuning on behavior 1 elicits both behaviors 2 and 3. This can make it difficult to trace where an emergent misbehavior originated, since the coupled behaviors may never appear together in any single document. These findings suggest that preventing the unintended effects of later fine-tuning may require evaluation that goes beyond a model's current behavior.