LLM Post-Training

LLM: Large Language Model

Follow this topic Ask about this topic

Momentum

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

Jul 13Week of Sep 28

Latest papers 175

All topics

Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines
May 1, 2026Hugo Abonizio, Filipe Rocha Lopes, Roberto Lotufo +1Knowledge Augmentation for Language Models Healthcare
FEEC, UNICAMP, Campinas, Brazil · NoHarm.ai, Brazil · Maritaca AI, Brazil
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors
May 1, 2026Chaohao Yuan, Chenghao Xiao, Yu Rong +2Task Arithmetic Language Model Merging
Department of Systems Engineering and Engineering Management, Chinese University of Hong Kong, Hong Kong, China · DAMO Academy, Alibaba Group, Hangzhou, China · Hupan Lab, Hangzhou, China +1
Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers
Apr 28, 2026Jan Dubiński, Jan Betley, Anna Sztyber-Betley +2Language Model Safety Evaluation Emergent Misalignment in Language Models
1Warsaw University of Technology · 4Truthful AI · University College London +2
Compute Aligned Training: Optimizing for Test Time Inference
Apr 27, 2026Adam Ousherovitch, Ambuj TewariTest-Time Optimization Inference-Time Scaling
Department of Statistics University of Michigan
SFT-then-RL Outperforms Mixed-Policy Methods for LLM Reasoning
Apr 26, 2026Alexis Limozin, Eduard Durech, Torsten Hoefler +2RL for Language Model Reasoning Policy Optimization
ETH AI Center, ETH Zürich · EPFL · Allen Institute for AI
Training a General Purpose Automated Red Teaming Model
Apr 24, 2026Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea +1Adversarial Attacks on LLMs LLM Red Teaming
NVIDIA
Bootstrapping Post-training Signals for Open-ended Tasks via Rubric-based Self-play on Pre-training Text
Apr 21, 2026Chengyu Huang, Sheng-Yen Chou, Zhengxin Zhang +1Self-Play RL LLM Post-Training
Department of Computer Science, Cornell University
Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning
Apr 19, 2026Zhiyin Yu, Bo Zhang, Qibin Hou +3Self-Training for Language Models RL for Language Model Reasoning
Peking University · Shanghai Artificial Intelligence Laboratory · Nankai University +1
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
Apr 18, 2026Sumeet Ramesh Motwani, Chuan Du, Aleksander Petrov +4LLM Post-Training RL Fine-Tuning
1X · University of Oxford
Evaluating the Progression of Large Language Model Capabilities for Small-Molecule Drug Design
Apr 17, 2026Shriram Chennakesavalu, Kirill Shmilovich, Hayley Weir +5LLM Evaluation Molecular Property Prediction
Prescient Design, Genentech, South San Francisco, CA, USA
Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO
Apr 17, 2026Arash Ahmadi, Parisa Masnadi, Sarah Sharif +3RL for Language Models Healthcare
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA · Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory, University of Oklahoma, Norman, OK, USA · Data Science and Analytics Institute, University of Oklahoma, Norman, OK, USA +3
Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation
Mar 18, 2026Philipp Normann, Andreas Happe, Jürgen Cito +1LLM Agent Security LLM Post-Training
TU Wien, Vienna, Austria
Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data
Mar 10, 2026Hyunji Nam, Haoran Li, Natasha JaquesLLM Personalization Mutual Information Maximization
Stanford University · Character AI · University of Washington
SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories
Jan 20, 2026Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava +2LLM Post-Training Automated Test Generation
Carnegie Mellon University · Nutanix · Stanford University
Aligning LLMs with Biomedical Knowledge using Balanced Fine-Tuning
Nov 26, 2025Zhenchao Tang, Fang Wang, Haohuai He +13LLM Alignment LLM Fine-Tuning
Tencent AI for Life Sciences Lab, Shenzhen, China. · Sun Yat-sen University, Shenzhen, China. · The Hong Kong Polytechnic University, Hong Kong SAR, China. +2
Value Drifts: Tracing Value Alignment During LLM Post-Training
Oct 30, 2025Mehar Bhatia, Shravan Nayak, Gaurav Kamath +4LLM Alignment Preference Optimization
Mila - Quebec AI Institute · McGill University · Université de Montréal +4
A Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
Oct 21, 2025Mengqi Li, Lei Zhao, Anthony Man-Cho So +2Self-Training for Language Models Mathematical Reasoning
The Chinese University of Hong Kong, Shenzhen · Shanghai Jiao Tong University · The Chinese University of Hong Kong
Strengthening LLMs for Tabular Prediction with Structural Priors
Oct 20, 2025Pengxiang Cai, Zihao Gao, Wanchen Lian +2Tabular Foundation Models RL for Language Models
The Hong Kong University of Science and Technology (Guangzhou) · Zhejiang University
LLP: LLM-Based Product Pricing in E-commerce
Oct 10, 2025Hairu Wang, Sheng You, Qiheng Zhang +5E-Commerce LLM Post-Training
Xianyu of Alibaba, Hangzhou, China · University of Science and Technology of China, Suzhou, China
Post-training for Efficient Communication via Convention Formation
Aug 8, 2025Yilun Hua, Evan Wang, Yoav ArtziLLM Evaluation LLM Post-Training
Department of Computer Science and Cornell Tech Cornell University
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
Jul 7, 2025Song Lai, Haohan Zhao, Rong Feng +9Continual Learning for LLMs LLM Post-Training
Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China · City University of Hong Kong, Hong Kong, China · School of Artificial Intelligence, Jilin University, Changchun, China +3
Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
Jul 2, 2025Ismail Labiad, Mathurin Videau, Matthieu Kowalski +4Privacy-Preserving Language Models Evolutionary Optimization
Meta FAIR · Université Paris-Saclay · LISN, Inria, CNRS +2
Dual-Difficulty Curriculum Learning for Direct Preference Optimization
Apr 10, 2025Mengyang Li, Haozhan Geng, Zhong Zhang +1Pairwise Preference Learning LLM Alignment
Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin Normal University Tianjin, China
Unified Text-Image Generation with Weakness-Targeted Post-Training
Date pendingJiahui Chen, Philippe Hansen-Estruch, Xiaochuang Han +7Unified Multimodal Models T2I Generation
FAIR at Meta · University of Texas, Austin · Work done at Meta +2
Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning
Date pendingPeng Du, Kiran Kamble, Rakshith Vasudev +5Supervised Fine-Tuning Tool-Augmented Language Model Agents
Writer AI Research, Writer, Inc.

May 1, 2026, cs.CLJ/K move · Enter open · S save

Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines

Hugo Abonizio, Filipe Rocha Lopes, Roberto Lotufo, Rodrigo Nogueira

FEEC, UNICAMP, Campinas, Brazil · NoHarm.ai, Brazil · Maritaca AI, Brazil

Knowledge Augmentation for Language Models Healthcare Clinical QA LLM Post-Training Clinical Language Model Evaluation Clinical NLP+1

Open paper AskPDF arXiv

Brazil's Unified Health System (SUS) relies on official clinical guidelines that define diagnostic criteria, treatments, dosages, and monitoring procedures for over 200 million citizens. Yet current LLMs perform poorly on this guideline-specific knowledge, and no benchmark evaluates clinical recall grounded in Brazilian Portuguese protocols. We address this gap by adapting Qwen2.5-14B-Instruct to the Brazilian clinical domain. From 178 official guidelines (~5.4M tokens), we generate ~70M tokens of synthetic data in three formats -- rephrases, wiki-style articles, and question-answer pairs -- using four generator LLMs. We then apply continual pre-training followed by Group Relative Policy Optimization (GRPO). We introduce HealthBench-BR, with 1,780 balanced true/false clinical assertions, and PCDT-QA, with 890 open-ended clinical questions scored by an LLM judge. Our best model achieves 83.9% on HealthBench-BR and 85.4% on PCDT-QA, outperforming GPT-5.2, Claude Sonnet 4.6, Gemini 3.1 Pro, and Google AI Overview's web-grounded RAG despite having only 14B parameters. Ablations show that generator diversity and reinforcement learning are critical to these gains. We release all datasets, benchmarks, and model weights to support reproducible clinical NLP research for Brazilian Portuguese. Code, data, and model weights are available at https://github.com/hugoabonizio/clinical-protocols-br

Momentum

Related topics

Latest papers 175