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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Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
May 27, 2026Mingze Wu, Abhinav Anand, Shweta Verma +1RL for Code Generation Offline RL
Technische Universitat, Darmstadt, Germany · Hessian Center for Artificial Intelligence, Darmstadt, Germany · National Research Center for Applied Cybersecurity ATHENE
Zipping the Thought: When and How Compressed Reasoning Data Works in LLM Post-Training
May 27, 2026Kohsei Matsutani, Gouki Minegishi, Takeshi Kojima +2Supervised Fine-Tuning LLM Post-Training
The University of Tokyo
Soft Specialists: $α$ -Rényi Ensembles for Uncertainty-Aware LLM Post-Training
May 26, 2026Paula Cordero-Encinar, Georgy Tyukin, Andrew B. DuncanLLM Uncertainty Estimation Language Model Ensembles
Department of Mathematics, Imperial College London · Bessemer AI
Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders
May 26, 2026Yi Jing, Zao Dai, Jinwu Hu +4RL for Language Model Reasoning LLM Post-Training
Tsinghua University
Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models
May 26, 2026Liyi Zhang, Akshay K. Jagadish, Brenden M. Lake +1Language Model Calibration LLM Uncertainty Estimation
Department of Computer Science Princeton University · Princeton AI Lab Princeton University · Department of Psychology and Computer Science Princeton University
Learning to Adapt SFT Data for Better Reasoning Generalization
May 26, 2026Lisong Sun, Li Wang, Chen Zhang +4Supervised Fine-Tuning LLM Post-Training
Beihang University · Tsinghua University · Nanyang Technological University +2
When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection
May 26, 2026Zhengyu Hu, Zheyuan Xiao, Linxin Song +10Language Model Distillation LLM Post-Training
University of Washington · University of Texas at Austin · University of Southern California +6
Composition Collapse: Stable Factual Knowledge Does Not Imply Compositional Reasoning
May 26, 2026Zhe Yu, Wenpeng Xing, Yunzhao Wei +4Compositional Reasoning Reasoning Evaluation
Binjiang Institute of Zhejiang University · Zhejiang University · Hong Kong Baptist University +2
Causal methods for LLM development and evaluation
May 25, 2026Dennis Frauen, Marie Brockschmidt, Konstantin Hess +10LLM Evaluation Causal Inference
MCML & LMU Munich, Munich, Germany · CMU, Pittsburgh, US · Imperial College London, London, UK +1
Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization
May 25, 2026Mengfan Liu, Da Zheng, Junwei Su +1GPU Kernel Optimization Large Language Model-Guided Optimization
The University of Hong Kong Hong Kong, China · Ant Group Hangzhou, China · University of Science and Technology of China Hefei, China
BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data
May 25, 2026Bo Zou, Chao XuSynthetic Data Generation for Language Models LLM Post-Training
Unlocking Proactivity in Task-Oriented Dialogue
May 21, 2026Azure Zhang, Ning Gao, Yuqin Dai +7LLM Post-Training Conversational Agents
1Keeta AI, Meituan · 2Independent Researcher · 3CUHK +1
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
Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?
May 16, 2026Prateek Rajput, Yewei Song, Iyiola E. Olatunji +2Personality Modeling in Language Models LLM Post-Training
University of Luxembourg, Esch-sur-Alzette, Luxembourg
Decoupling KL and Trajectories: A Unified Perspective for SFT, DAgger, Offline RL, and OPD in LLM Distillation
May 16, 2026Anhao Zhao, Haoran Xin, Yingqi Fan +3Reasoning Distillation On-Policy Distillation
Eastern Institute of Technology, Ningbo · Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou) · Shanghai Jiao Tong University +1
Convex Dataset Valuation for Post-Training
May 15, 2026Siqi Zeng, Christopher Jung, Rui Li +7LLM Post-Training Data Valuation
Department of Computer Science, University of Illinois Urbana-Chamapign, Urbana, IL, USA · Meta, Menlo Park, CA, USA
GRACE: Gradient-aligned Reasoning Data Curation for Efficient Post-training
May 13, 2026Junjie Li, Ziao Wang, NingXuan Ma +2Training Data Selection LLM Post-Training
Harbin Institute of Technology, Shenzhen, China · Hong Kong Baptist University, China
Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators
May 12, 2026Heejin Do, Shashank Sonkar, Mrinmaya SachanLLM Evaluation LLM Sycophancy
ETH Zürich, ETH AI Center · University of Central Florida · ETH Zürich
Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
May 12, 2026Chengqian Zhang, Wei Zhu, Kyumin LeeLow-Rank Adaptation LLM Post-Training
Worcester Polytechnic Institute · University of Hong Kong
ConFit v3: Improving Resume-Job Matching with LLM-based Re-Ranking
May 10, 2026Xiao Yu, Ruize Xu, Chengyuan Xue +6Reinforcement Learning Learning to Rank
Columbia University · New York, NY, USA · John Hopkins University +6
Geometry Conflict: Explaining and Controlling Forgetting in LLM Continual Post-Training
May 10, 2026Yuanyi Wang, Yifan Yang, Su Lu +9Continual Learning for LLMs Model Merging
The Hong Kong Polytechnic University, PolyU · The Hong Kong University of Science and Technology, HKUST · PolyU-Daya Bay Technology and Innovation Research Institute
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
May 10, 2026Taekhyun Park, Yongjae Lee, Dohee Kim +1Recurrent Transformers LLM Post-Training
Department of Data Science Pusan National University Busan, Republic of Korea · Department of Industrial Engineering Pusan National University Busan, Republic of Korea · Department of Artificial Intelligence Engineering Changwon National University Changwon, Republic of Korea
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
Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control
May 8, 2026Ali Taghibakhshi, Ruisi Cai, Saurav Muralidharan +17LLM Compression LLM Post-Training
NVIDIA
Self-Consolidating Language Models: Continual Knowledge Incorporation from Context
May 8, 2026Zekun Wang, Anant Gupta, Zihan Dong +1Continual Learning for LLMs Long-Context Language Modeling
Georgia Institute of Technology
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
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
Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training
May 6, 2026Hengyu Shi, Tianyang Han, Peizhe Wang +3Efficient Language Model Training LLM Post-Training
1Independent Researcher · 2D4 Lab · 3Southeast University
Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
May 4, 2026Ishaan Watts, Catherine Li, Sachin Goyal +2Language Model Pretraining Sharpness-Aware Minimization
Carnegie Mellon University
Iterative Finetuning is Mostly Idempotent
May 1, 2026Zephaniah Roe, Jack Sanderson, Dang Nguyen +5Supervised Fine-Tuning LLM Post-Training
University of Chicago

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Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning

Mingze Wu, Abhinav Anand, Shweta Verma, Mira Mezini

Technische Universitat, Darmstadt, Germany · Hessian Center for Artificial Intelligence, Darmstadt, Germany · National Research Center for Applied Cybersecurity ATHENE

RL for Code Generation Offline RL LLM Post-Training LLM Code Generation

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Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for code generation involves LLM inference and verification of the generated output, which can take considerable time and resources. In this paper, we explore the application of offline RL to code-generating models by leveraging existing code datasets. Our experiments demonstrate that offline RL is an effective training strategy for improving LLM performance. We show that offline RL can be especially beneficial for small LLMs and challenging coding problems.

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