Efficient Training of Robust Traditional Chinese LLaMA-1B on a Single Consumer GPU: Continual Pre-training, SFT, and DPO
National Tsing Hua University（台湾清华大学）；Accton Technology Corporation（智邦科技公司）；National Yang Ming Chiao Tung University（阳明交通大学）；University of Taipei（台北市立大学）
Yu-Cheng Chih, Ming-Tao Duan, Yong-Hao Hou
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17 pages, 1 figures, 2 tables. Technical report. Introduces PureTC-1B, an adapter-based pipeline for stabilizing Small Language Models in Traditional Chinese using CPT, SFT, and DPO
Small Language Models (SLMs) enable cost-effective, on-device and latency-sensitive AI applications, yet their deployment in Traditional Chinese (TC) remains hindered by token-level instability - models unpredictably emit non-TC characters or code-switch into other languages. We address this practical reliability gap by creating PureTC-1B, a three-stage stabilization pipeline for Llama-3.2-1B-Instruct (an open-weight, instruction-tuned model released by Meta) using parameter-efficient LoRA adapters. Our method combines Continual Pre-Training (CPT) on TC-centric corpora, Supervised Fine-Tuning (SFT) with instruction data, and Direct Preference Optimization (DPO) using TC-adherence preferences to improve monolingual robustness without full-model retraining. On a benchmark designed to simulate real-world usage, PureTC-1B achieves a 51.3% relative reduction (micro-average) in non-TC output tokens versus the base model. On a Named Entity Translation (NET) task, PureTC-1B further reduces incorrect-language tokens by 77.2% relative to Llama-3B and 57.2% relative to Qwen-1.5B, indicating that robust TC adherence is attainable even at the 1B scale. The pipeline is reproducible, adapter-only, and hardware-friendly, offering practitioners a practical recipe to enhance language stability for TC and potentially other non-English languages.
Learning When to Reason for Text-to-SQL via SFT and DPO
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电气与计算机工程系）；AI Center, Samsung Electronics（三星电子人工智能中心）；AIIS, ASRI, INMC, ISRC, and IPAI, Seoul National University（首尔国立大学人工智能研究所、高级科学研究所以及综合纳米技术中心、信息存储研究中心和人工智能平台研究所）
Manacá-1B-Instruct-2: Reproducible Safety Alignment for a Native Brazilian-Portuguese LLM. Negative DPO Results, a Controlled Composition Scan, and a Silent Failure Mode of the LLM Judge
National Laboratory for Scientific Computing (LNCC)（国家科学计算实验室）；Institute of Artificial Intelligence（人工智能研究所）
Topology-Enhanced Alignment for Large Language Models: Trajectory Topology Loss and Topological Preference Optimization
School of Computing and Intelligent Innovation, Fudan University（复旦大学计算与智能创新学院）；School of Economics and Management, Tongji University（同济大学经济与管理学院）；College of Information Technology, Shanghai Ocean University（上海海洋大学信息学院）
From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations
TCS Research（TCS研究）；Department of Computer Science and Engineering, Indian Institute of Technology Patna（印度理工学院帕纳布计算机科学与工程系）；Indian Institute of Technology Patna（印度理工学院帕纳布）；University of Innsbruck（因斯布鲁克大学）
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
Harvard T.H. Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Peking University（北京大学）；University of Oxford（牛津大学）；Alibaba Group（阿里巴巴集团）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Ant Group（蚂蚁集团）；Tianyi Traffic Technology（天翼交通科技）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Qwen LLM Application Team, Alibaba（阿里巴巴Qwen大模型应用团队）；Stanford University（斯坦福大学）
Northeastern Uni at Multilingual Counterspeech Generation: Enhancing Counter Speech Generation with LLM Alignment through Direct Preference Optimization
Capital One（第一资本）；Northeastern University（东北大学）；Apple Inc.（苹果公司）
Safety Alignment as Continual Learning: Mitigating the Alignment Tax via Orthogonal Gradient Projection
School of Life Sciences, IDG/McGovern Institute for Brain Research（生命科学学院，IDG/麦戈文脑科学研究所）；Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua-Bosch Joint ML Center, THBI Lab, BNRist Center（计算机科学与技术系，人工智能研究所，清华大学-博世联合机器学习中心，THBI实验室，BNRist中心）；Tsinghua University, Beijing, China（清华大学，北京，中国）
AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Models
Instituto Politécnico Nacional（墨西哥政治技术学院）；University of South Florida（佛罗里达州立大学）；Saarland University（萨尔兰大学）；Imperial College London（伦敦帝国理工学院）；University of Hamburg（汉堡大学）
