Language Model Post-Training

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23 papers in the last four weeks, up 283% on the four weeks before. 0.2% of all new papers.

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Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Jun 13, 2026Ang Li, Ben Liu, Bin Han +215Efficient Language Model Training Linear Attention
FastMix: Fast Data Mixture Optimization via Gradient Descent
Jun 12, 2026Haoru Tan, Sitong Wu, Yanfeng Chen +5Language Model Pretraining Data Mixture Optimization
University of Hong Kong · 2Hunyuan LLM, Tencent · 3Chinese University of Hong Kong
AudioDER: A Deduplication-Enhanced Reasoning Dataset for Post-Training Large Audio-Language Models
Jun 12, 2026Hui Geng, Yi Su, Han Yin +7Audio Reasoning Audio-Language Models
College of Computer Science and Technology, National University of Defense Technology Changsha, Hunan, China · Korea Advanced Institute of Science and Technology (KAIST) Korea · Shanghai Jiaotong University Shanghai, China
Achieving Precise Text-To-Cypher Via Grounded Knowledge Graph Data Generation
Jun 12, 2026Francesco Cazzaro, Jessica Lennon, Ariadna QuattoniText-to-Cypher Synthetic Data Generation for Language Models
Universitat Politècnica de Catalunya, Barcelona, Spain
The Culture Funnel: You Can't Align What isn't in the Data
Jun 11, 2026Ananya Sahu, Mehrnaz Mofakhami, Daniel D'Souza +3Cultural Alignment Cultural Bias in Language Models
CohereCorrespondingLabs
PolyAlign: Conditional Human-Distribution Alignment
Jun 11, 2026L. D. M. S. Sai Teja, Ufaq Khan, Sathira Silva +2Multilingual Language Models LLM Alignment
NIT Silchar, India · MBZUAI, Abu Dhabi, UAE
Rubric-Guided Self-Distillation: Post-Training Without Rubric Verifiers
Jun 10, 2026MohammadHossein Rezaei, Anas Mahmoud, Zihao Wang +6Rubric-Based RL Language Model Distillation
Scale AI
It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO
Jun 9, 2026Naihao Deng, Yilun Zhu, Naichen Shi +2LLM Alignment Social Bias in Language Models
University of Michigan · Northwestern University
Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach
Jun 4, 2026Zhihao Lin, Ziqi Zhu, Hao Huang +2Synthetic Data Generation for Language Models Language Model Post-Training
1Amazon Web Services (AWS) · 2Peking University
Self-supervised User Profile Generation for Personalization
Jun 3, 2026Clark Mingxuan Ju, Yuwei Qiu, Tong Zhao +1LLM Personalization Self-Supervised Learning
Snap Inc. · Bellevue, WA USA
Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)
Jun 3, 2026Nizar Islah, Istabrak Abbes, Irina Rish +2Test-Time Scaling LLM Routing
1Mila - Quebec AI Institute · 2Université de Montréal · 3Polytechnique Montréal +1
Learning What to Learn: Stage-Specific Data Sets for SFT-then-RL in Small Language Model Reasoning
Jun 3, 2026Chongyang He, Rui Zhang, Zixuan Wang +1Supervised Fine-Tuning RL for Language Model Reasoning
1Tsinghua University · 2National University of Singapore · 3DiDi +1
AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training
Jun 1, 2026Liu Qing, Ou Wu, Yi DuLLM Fine-Tuning Language Model Post-Training
Hangzhou Institute for Advanced Study
LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification
May 30, 2026Shefayat E Shams Adib, Ahmed Alfey Sani, Md Hasibur Rahman Alif +1Class-Imbalanced Learning Quantization-Aware LoRA
Department of Computer Science and Engineering, Islamic University of Technology, Dhaka, Bangladesh
Post-Training Recipe, More Than Model Family, Shapes Multi-Agent LLM Conversational Behavior
May 30, 2026Luyang Zhang, Jialu Wang, Fei Xue +1Language Model Post-Training Multi-LLM Collaboration
Carnegie Mellon University · University of California, Santa Cruz · Independent Researcher
Consolidating Rewarded Perturbations for LLM Post-Training
May 29, 2026Zheyu Zhang, Shuo Yang, Gjergji KasneciRL Post-Training Language Model Post-Training
Technical University of Munich
LEAF: Growing Trees Without Branching for Speech-Aware Large Language Model Post-Training
May 29, 2026Argyrios Gerogiannis, Yekaterina Yegorova, Mark Hasegawa-Johnson +1RL for Language Models Credit Assignment in RL
University of Illinois, Urbana-Champaign
Measuring, Localizing, and Ablating Alignment Signatures in LLMs
May 28, 2026Aniket Anand, Janvijay Singh, Zhewei Sun +2LLM Alignment AI-Generated Text Detection
University of Chicago · University of Illinois at Urbana-Champaign · Toyota Technological Institute at Chicago
Exploring Autonomous Agentic Data Engineering for Model Specialization
May 28, 2026Yujie Luo, Xiangyuan Ru, Jingsheng Zheng +10Large Language Model-Guided Optimization Synthetic Data Generation for Language Models
Zhejiang University · Platform and Content Group, Tencent
Self-Improving Language Models with Bidirectional Evolutionary Search
May 27, 2026Guowei Xu, Zhenting Qi, Huangyuan Su +4Evolutionary Optimization LLM Planning
1Harvard University · 2MIT
Boundary Suppression Asymmetry in Post-trained Assistants: Over-expansion as a Controllability Cost
May 27, 2026Jiarui HanLanguage Model Steering Language Model Post-Training
Narrative Flattening: How Post-Training Compresses Thematic, Affective, and Stylistic Variation in LLM Fiction
May 27, 2026Zehan Li, Yutong Zhu, Siyang Wu +2Language Model Post-Training Text Generation
Knowledge Lab, University of Chicago
Trait-Aware Policy Optimization for Autoregressive Multi-Trait Essay Scoring
May 25, 2026Zhengyang Wang, Sanwoo Lee, Jiaxin Wang +3Automated Essay Scoring Language Model Post-Training
Peking University · Baidu Inc.
From Simulation to Enaction: Post-trained language models recognize and react to their own generations
May 25, 2026Asvin G., Jack LindseyLanguage Model Introspection LLM Uncertainty Estimation
Institute for Advanced Study, Princeton · Anthropic
It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt
May 22, 2026Stuart Bladon, Brinnae BentPolitical Bias in Language Models Language Model Bias Evaluation
Duke University
OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations
May 22, 2026Jiangwang Chen, Bowen Zhang, Zixin Song +4Long-Term Conversational Memory Efficient Language Model Inference
1Tsinghua University · 2Qwen Applications Business Group of Alibaba
Reducing Political Manipulation with Consistency Training
May 21, 2026Long Phan, Devin Kim, Alexander Pan +3Social Bias in Language Models Political Bias in Language Models
Center for AI Safety · UC Berkeley
Post-Training is About States, Not Tokens: A State Distribution View of SFT, RL, and On-Policy Distillation
May 21, 2026Dong NieSupervised Fine-Tuning RL for Language Model Reasoning
Independent Researcher
EmbGen: Teaching with Reassembled Corpora
May 19, 2026Arun K Lenin, Kai Rouse, Andrea Nicastro +1Domain Adaptation for LLMs Synthetic Data Generation for Language Models
Commonwealth Bank of Australia Sydney, Australia
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM $Δ$ Integration into Upcycled MoE
May 18, 2026Hao Zhou, Tianhao Li, Zhijun Wang +6Multilingual Language Models Mixture-of-Experts Language Models
National Key Laboratory for Novel Software Technology, Nanjing University · Tongyi Lab, Alibaba Group · Zhejiang University

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Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Ang Li, Ben Liu, Bin Han, Bin Hu+214

Efficient Language Model Training Linear Attention LLM Agent Training Efficient Language Model Inference Language Model Post-Training RL for LLM Agents

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Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy. In this report, we present Ling-2.6 and Ring-2.6, a family of models designed to address this challenge at scale. Ling-2.6 is optimized for instant response generation and high capability per output token, whereas Ring-2.6 is tailored for deeper reasoning and more advanced agentic workflows. Instead of training from scratch, we upgrade the Ling-2.0 base model through architectural migration pre-training and large-scale post-training. This upgrade is guided by a unified co-design of model architecture, optimization objectives, serving systems, and agent training environments, enabling improvements in both model capability and deployment efficiency. At the architectural level, we introduce a hybrid linear attention design that integrates Lightning Attention with MLA, improving the efficiency of long-context training and decoding. To further enhance token efficiency, we optimize capability per output token through Evolutionary Chain-of-Thought, Linguistic Unit Policy Optimization, bidirectional preference alignment, and shortest-correct-response distillation. For agentic capabilities, we propose KPop, a reinforcement learning framework designed to support stable training of Ring-2.6-1T on large-scale environment-grounded data. KPop improves training efficiency through asynchronous scheduling across coding, search, tool use, and workflow execution, enabling scalable learning from complex agent-environment interactions. Together, Ling-2.6 and Ring-2.6 provide a practical pathway toward efficient, scalable, and open agentic systems. We open-source all checkpoints in the 2.6 family to support further research and development in practical agentic intelligence.

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