LLM Post-Training

LLM: Large Language Model

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29 papers in the last four weeks, up 81% on the four weeks before. 0.3% of all new papers.

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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
SAPD: Step-Aligned Privileged Distillation
Oct 7, 2026Tianle Wang, Jiayu Liu, Ruizhi Zhao +1LLM Post-Training Language Model Distillation
Department of Data Science, City University of Hong Kong · Hong Kong Institute of AI for Science, City University of Hong Kong · Shenzhen University
When Rank Rises as LLMs Degrade
Oct 7, 2026Zhaohui Geoffrey WangLLM Post-Training Representation Geometry in Language Models
University of Southern California
CM-DPO: Constraint-Margin Direct Preference Optimization for LLM Planning
Oct 6, 2026Rabimba Karanjai, Qun Gu, Hemanth Hegadehalli Madhavarao +8Direct Preference Optimization LLM Planning
PayPal AI Lab
Harmful SFT Leaves a Continuous Trace in LLM Checkpoint Updates
Oct 5, 2026Ziqun Bao, Xinyu Zhang, Yuchen Shao +1LLM Auditing LLM Safety
SEI, East China Normal University
The Assistance Dilemma: Learning to Teach via Multi-Turn Reinforcement Learning
Oct 5, 2026Jakub Macina, Manu Kapur, Mrinmaya SachanRL for Language Models Intelligent Tutoring Systems
ETH Zurich
ImproveAnyTask: An Autonomous Post-Training Harness for Iterative Model Self-Improvement
Oct 5, 2026Xingbo Yao, Xiaoman Wang, Zhengwu Lei +11LLM Fine-Tuning LLM Post-Training
HKUST(GZ) · Xiaohongshu Inc. · ECNU +3
Dynamic Minimax Regret Optimization for Robust LLM Post-Training
Oct 5, 2026Chengbo Zang, Haoyu Dong, Mehmet Kerem Turkcan +3Dynamic Regret Minimization Distributionally Robust Optimization
Department of Electrical Engineering, Columbia University · Department of Computer Science & Engineering, University of California, Santa Cruz · Department of Civil Engineering, Columbia University
The Functional Structure of Post-Compression Recovery in Low-Rank LLMs
Oct 4, 2026Zishan Shao, Liang Tian, Georgiy Zemlevskiy +10LLM Compression Low-Rank Compression
Duke University · Wake Forest University · Carnegie Mellon University +2
The Missing Primitive: Diagnosing and Repairing Mathematical Reasoning in Large Language Models
Oct 1, 2026Shuo Xing, Zilin Dai, Chengyuan Qian +7Mathematical Reasoning Benchmarks Self-Distillation
Texas A&M University · Harvard University · Vanderbilt University +3
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
Sep 30, 2026Kunlun Zhu, Xuyan Ye, Yibo Li +3Agent Failure Analysis LLM Agent Training
Apodex
IrekoGPT: Turning Structured Pruning into Post-Hoc Slimmable LLMs
Sep 30, 2026Pietro Moriello, Pietro Buzzega, Angelo Porrello +1LLM Compression LLM Inference Acceleration
AImageLab, University of Modena and Reggio Emilia, Italy
From Search to Signal: Online Post-Training in Automatic Heuristic Design
Sep 30, 2026Yilun Yuan, Tianyu Zhou, Zhenzhou TangAutomated Heuristic Design Reinforcement Learning with Verifiable Rewards
Wenzhou University
Aligned Data Can Induce Misalignment via Context Confusion
Sep 29, 2026Yavuz Bakman, Duygu Nur Yaldiz, Baris Askin +4LLM Alignment LLM Post-Training
University of Southern California · Carnegie Mellon University · Amazon +1
Beyond Compression: Diagnosing How Post-Training Changes Mathematical Reasoning
Sep 29, 2026Hongyang Li, Yiming Zhu, Xiao Li +3Language Model Distillation RL Post-Training
University of Luxembourg · Seafill Open-Source Community · Université Paris-Saclay
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
Sep 29, 2026Bo Mao, Hang He, Linting Wang +17LLM Agent Self-Improvement LLM Post-Training
East China Normal University · Shanghai Innovation Institute · Fudan University +2
Frontier Learning: Training LLM Reasoners at the Edge of Capability
Sep 28, 2026Robin Faro, Shyam Sundhar Ramesh, Ilija Bogunovic +1RL for Language Model Reasoning LLM Post-Training
University College London London, UK · University of Basel Basel, Switzerland
LLMs learn different forms of metacognition when trained to predict their own accuracy
Sep 27, 2026Nicolas Yax, Stefano Palminteri, Pierre-Yves OudeyerConfidence Estimation in Language Models Language Model Calibration
LNC2, INSERM, Paris, France · DEC, ENS, PSL, Paris, France · Flowers AI & CogSci Lab +1
How code helps different tasks? A decompositional lens on LLM post-training
Sep 27, 2026Zheng Yu, Yiwei Li, Yishen Chen +4LLM Fine-Tuning Instruction-Tuning Data Selection
Sun Yat-sen University · Shenzhen Loop Area Institute · The Chinese University of Hong Kong, Shenzhen +1
A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards
Sep 27, 2026Andreas Plesner, Curtis Northcutt, Francisco Guzmán +1Reward Modeling LLM Reliability
ETH Zurich · Handshake AI
Search-Aware Reinforcement Learning for Multi-Component Query Understanding in Roblox Game Search
Sep 24, 2026Nayoung Choi, Shengjian Chen, Xiaokai Wei +6Reinforcement Learning RL for IR
Emory University Atlanta, Georgia, USA · Roblox Corporation San Mateo, California, USA
Rufus-Air: An Open LLM Post-Training Recipe
Sep 24, 2026Chia-Yuan Chang, Renyuan Cheng, Rui Feng +19LLM Fine-Tuning RL for Language Model Reasoning
Amazon
Thinking Leakage: A Causal Audit of NoThink Post-Training in Hybrid Reasoning Models
Sep 23, 2026Zehao Liu, Vasant G. HonavarLLM Post-Training Causal Interventions in Language Models
College of Information Sciences and Technology Pennsylvania State University
Toolcompass: Guiding Tool Trialing, Not Suppressing It
Sep 22, 2026Junlin Fang, Chong Zhang, Do Nguyen-Thanh +3Representation Learning Tool-Augmented Language Model Agents
College of Computing and Data Science, Nanyang Technological University · Zhejiang University · Australian Artificial Intelligence Institute, University of Technology Sydney +1
TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Tasks
Sep 21, 2026Bohao Wang, Chenwei Wu, Hang Zou +7LLM Grounding RL for Language Model Reasoning
College of Information Science and Electronic Engineering, Zhejiang University, 310027 Hangzhou, China · Research Institute for Digital Future, Khalifa University, P O Box 127788, Abu Dhabi, UAE · Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109-2122, USA +1
SupportCal: Label-Free Calibration of Post-Trained LLMs via Reference Support and Corroboration
Sep 21, 2026Linhan Luo, Lequan Lin, Dai Shi +3Post-Hoc Calibration Language Model Calibration
The University of Sydney · University of Cambridge · The University of Adelaide
Measuring the Assistant's Harmlessness Preferences on the User Turn
Sep 20, 2026Jord NguyenLanguage Model Safety Evaluation LLM Safety
Style-Debiased DPO: Updating LLM Knowledge with Factuality-Aware Synthetic Preference Data
Sep 15, 2026Takayuki Yamamoto, Daisuke KawaharaKnowledge Editing Direct Preference Optimization
Waseda University
Inoculation Midtraining with Learned Neologisms
Sep 14, 2026Kyle O'Brien, Edward James Young, Puria Radmard +4LLM Alignment LLM Safety
Geodesic Research · OpenAI · UK AI Security Institute
Occamy-1.0: Open Pareto-frontier 35B Intelligence for Co-work
Sep 14, 2026Wenhui Chen, Shiwen Cheng, Hao Dong +40LLM Agent Training LLM Post-Training

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Successive Training Stages and Large Language Model Persuasion: Effects of Misalignment, Supervised Fine-Tuning, and Preference Optimization

Antony Dalmiere, Pascal Marchand, Guillaume Auriol, Vincent Nicomette

LAAS-TRUST · LAAS-TRUST, INSA Toulouse · LAAS-TSF, LAAS

Persuasion in Language Models LLM Post-Training Supervised Fine-Tuning Preference Optimization

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Large language models (LLMs) can be tuned to influence human attitudes, yet the respective contributions of successive post-training stages remain un-clear. This study examines how three successive training stages affect LLM persuasiveness: (1) misalignment through supervised fine-tuning (SFT) on conspiracy data, (2) additional persuasive SFT on argumentative data, and (3) Identity Preference Optimization (IPO), a preference-optimization method. A total of 835 participants recruited on Prolific were randomly assigned to five between-subject conditions (neutral text, conspiracy-trained model, persuasion-trained model, preference-optimized model, and GPT-4) and were exposed to texts on 10 divisive political issues, personalized from their individual profiles in all model conditions. Attitude change was measured as the difference between pre- and post-exposure positions on continuous Likert scales and analyzed with an analysis of covariance (ANCOVA). A significant condition x baseline-attitude interaction, F (4, 825) = 5.33, p < .001, indicated that training effects depended on participants' initial attitudes. Persuasive SFT produced greater attitude change than conspiracy training alone, d = 0.30, whereas IPO provided no additional benefit, d = 0.03, and GPT-4 did not differ from neutral text, d = --0.01. These results show that targeted supervised training on persuasive data increases LLM persuasiveness, whereas preference optimization yields no significant gains beyond it.

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