Language Model Post-Training

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Language model post-training studies how to turn a pretrained model into a more useful, reliable, or specialized system without training it from scratch. Common approaches include supervised fine-tuning on demonstrations, preference optimization such as DPO, reinforcement learning with human or verifiable rewards, and distillation from other models. Researchers apply these methods to tasks from mathematical reasoning and coding to translation, tool use, and domain-specific work, evaluating both task performance and qualities such as calibration, safety, and diversity. A central tension is that gains on targeted tasks can come with weaker generalization or loss of capabilities, and which methods work best often depends on the model, data, and evaluation setting.

Latest papers

197 papers47 papers in the last four weeks, up 104% on the four weeks before. 0.3% of all new papers.

Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare
Apr 30, 2026Jasmine Brazilek, Harper DunnMoral Reasoning in Language Models Language Model Post-Training
Compassion Aligned Machine Learning (CaML) · Independent researcher
Helpfulness Hurts: Domain-Dependent Degradation of Mid-Trained Compassion Values Under Post-Training
Apr 30, 2026Jasmine Brazilek, Juliana SeawellMoral Reasoning in Language Models RL Post-Training
†Compassion Aligned Machine Learning (CaML)
Instruction-Guided Poetry Generation in Arabic and Its Dialects
Apr 30, 2026Abdelrahman Sadallah, Kareem Elozeiri, Mervat Abassy +5Arabic NLP Controllable Text Generation
Mohamed bin Zayed University of Artificial Intelligence
Backtranslation Augmented Direct Preference Optimization for Neural Machine Translation
Apr 28, 2026Mehrdad Ghassabi, Spehr Rajabi, Hamidreza Baradaran Kashani +3Back-Translation Language Model Post-Training
Faculty of Computer Engineering University of Isfahan Isfahan, Iran · School of Computer Science University of Windsor Windsor, Canada
$\mathcal{S}^2$ IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction
Apr 25, 2026Bingfeng Chen, Chenjie Qiu, Yifeng Xie +3LLM Fine-Tuning Aspect-Based Sentiment Analysis
School of Computer Science, Guangdong University of Technology · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Peng Cheng Laboratory +1
Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought
Apr 24, 2026Keshav Ramji, Tahira Naseem, Ramón Fernandez AstudilloEfficient Language Model Reasoning Implicit Reasoning in Language Models
IBM Research AI
On Reasoning Behind Next Occupation Recommendation
Apr 23, 2026Shan Dong, Palakorn Achananuparp, Hieu Hien Mai +3Large Language Model-Based Recommendation Language Model Post-Training
Singapore Management University · Columbia University
Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech
Apr 22, 2026Siqi Ouyang, Shuoyang Ding, Oleksii Hrinchuk +4RL for Language Models Speech Translation
1Carnegie Mellon University · 2NVIDIA
Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
Apr 21, 2026Yunbo Long, Tejumade Afonja, Guangya Hao +2Tabular Foundation Models Group Relative Policy Optimization
Department of Engineering, University of Cambridge · CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · The Alan Turing Institute, London
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
Semantic Step Prediction: Multi-Step Latent Forecasting in LLM Reasoning Trajectories via Step Sampling
Apr 20, 2026Yidi YuanNeural Representation Geometry Language Model Post-Training
Home Team Science and Technology Agency
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
PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs
Apr 19, 2026Yuting Huang, Yinghao Hu, Qian Xiao +7LLM Grounding Language Model Post-Training
Zhejiang University, Hangzhou, China · Tongyi Lab, Alibaba Group, Hangzhou, China · Zhejiang Zhengfa Xinxi Guanli Zhongxin, Hangzhou, China
OPSDL: On-Policy Self-Distillation for Long-Context Language Models
Apr 19, 2026Xinsen Zhang, Zhenkai Ding, Tianjun Pan +4Long-Context Language Modeling On-Policy Self-Distillation
Baidu Inc.
Jupiter-N Technical Report
Apr 19, 2026George DraysonLLM Alignment Cultural Alignment
Locai Labs, University College London
The Illusion of Certainty: Decoupling Capability and Calibration in On-Policy Distillation
Apr 18, 2026Jiaxin Zhang, Xiangyu Peng, Qinglin Chen +3On-Policy Self-Distillation Language Model Calibration
Salesforce AI Research
Where does output diversity collapse in post-training?
Apr 17, 2026Constantinos Karouzos, Xingwei Tan, Nikolaos AletrasOutput Diversity in Language Models Language Model Post-Training
School of Computer Science University of Sheffield, UK
SFT-GRPO Data Overlap as a Post-Training Hyperparameter for Autoformalization
Apr 15, 2026Xiaole Su, Kasey Zhang, Andy LyuLanguage Model Post-Training LLM Post-Training
Osmosis AI
Peer-Predictive Self-Training for Language Model Reasoning
Apr 14, 2026Shi Feng, Hanlin Zhang, Fan Nie +2Self-Training for Language Models Language Model Post-Training
Harvard University · Stanford University
English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training
Apr 14, 2026Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin +2Cross-Lingual Reasoning in Language Models Low-Resource Language Processing
UC Santa Barbara · Amazon
BITS Pilani at SemEval-2026 Task 9: Structured Supervised Fine-Tuning with DPO Refinement for Polarization Detection
Apr 13, 2026Atharva Gupta, Dhruv Kumar, Yash SinhaSocial Media Analysis Language Model Post-Training
Birla Institute of Technology and Science, Pilani, India
How Reasoning Evolves from Post-Training Data: An Empirical Study Using Chess
Apr 6, 2026Lucas Dionisopoulos, Nicklas Majamaki, Prithviraj AmmanabroluLanguage Model Post-Training RL for Language Model Reasoning
Department of Computer Science and Engineering, University of California San Diego, San Diego, United States.
The Role of Generator Access in Autoregressive Post-Training
Apr 6, 2026Amit Kiran RegeLanguage Model Post-Training Autoregressive Generation
Department of Computer Science, University of Colorado Boulder, Boulder, Colorado 80309
How Does "English (US)" Become the Default? Triangulating Structural Bias Towards American English Across the LLM Pipeline
Apr 5, 2026Mir Tafseer Nayeem, Davood RafieiLanguage Model Pretraining LLM Auditing
Department of Computing Science University of Alberta
Random Is Hard to Beat: Active Selection in online DPO with Modern LLMs
Apr 3, 2026Giyeong Oh, Junghyun Lee, Jaehyun Park +3Active Preference Learning Language Model Post-Training
Seoul National University · Ewha Womans University · KAIST +1
Aligned, Orthogonal or In-conflict: When can we safely optimize Chain-of-Thought?
Mar 31, 2026Max Kaufmann, David Lindner, Roland S. Zimmermann +1CoT Monitoring CoT Supervision
Google DeepMind
Backdoor Attacks on Decentralised Post-Training
Mar 31, 2026Oğuzhan Ersoy, Nikolay Blagoev, Jona te Lintelo +3LLM Backdoor Attacks Language Model Post-Training
Gensyn · University of Neuchâtel · Radboud University +3
Rethinking Easy-to-Hard: Limits of Curriculum Learning in Post-Training for Deductive Reasoning
Mar 28, 2026Maximilian Mordig, Andreas Opedal, Weiyang Liu +1Curriculum Learning Language Model Post-Training
Max Planck Institute for Intelligent Systems, Tübingen · ETH Zürich · The Chinese University of Hong Kong
Same Voice, Different Lab: On the Homogenization of Frontier LLM Personalities
Mar 20, 2026Avinash Krishna, Kalyana Chadalavada, Unso Eun Seo JoPersonality Modeling in Language Models Behavioral Consistency of LLM Agents
Independent · Anthropic · Cornell University
HumorGen: Cognitive Synergy for Humor Generation in Large Language Models via Persona-Based Distillation
Mar 19, 2026Edward Ajayi, Prasenjit MitraHumor Explanation Generation Text Generation
Carnegie Mellon University Africa

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Assert, don't describe: Linguistic features that shift LLM reasoning about animal welfare

Jasmine Brazilek, Harper Dunn

Compassion Aligned Machine Learning (CaML) · Independent researcher

Moral Reasoning in Language Models Language Model Post-Training LLM Training LLM Reasoning

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Animal-welfare advocates produce a lot of writing, and increasingly that writing trains the language models that millions of people then ask about animal welfare. Using vocabulary-matched stance-contrast probes on a held-out animal-welfare benchmark, we measure how each of ten linguistic features changes Llama-3.2-1B's preference for pro-animal-welfare reasoning when used as fine-tuning data. Eight of the ten features produce statistically significant shifts. Seven move the model toward stronger pro-animal-welfare reasoning: assertive certainty, explicit moral vocabulary, emotion words, evaluative claims, narrative structure, depicted harm severity, and immediate temporal framing. Two move it the other way: hedged language and concrete sensory description both dilute the pro-animal-welfare stance. First-person perspective has no statistically significant effect. The practical recommendation for anyone writing animal-welfare text that may end up in LLM training corpora: assert a position rather than describe a scene neutrally. The features that shift the model are the ones that make the writer's position explicit; the features that dilute it hold animal-welfare content but withhold stance.