Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv AI

Jul 22

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.

By Weifeng Sun, Ye Fan, Yuchen Chen, Gou Tan, Jieke Shi, Yuan Yidi, Swee Liang Wong, Jonathan Pan, David Lo

llms fine-tuning benchmarks

arXiv AI

Jul 22

Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification

arXiv:2607. 19122v1 Announce Type: cross Abstract: Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge within a pre-trained model and to the possibility of reducing the computational cost of training.

By Simone Milani

fine-tuning safety

arXiv AI

Jul 22

Federated Lightweight Fine-Tuning

arXiv:2607. 18343v1 Announce Type: cross Abstract: Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor.

By Radhakrishna Achanta, Will Reed

fine-tuning

arXiv AI

Jul 22

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

arXiv:2607. 18283v1 Announce Type: cross Abstract: Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopmental abnormalities.

By Alessandro Di Matteo, Sara Moccia, Giuseppe Rizzo, Gianpaolo Grisolia, Ricciarda Raffaelli, Lorenzo Vasciaveo, Francesco D'Antonio, Maria Chiara Fiorentino

computer-vision fine-tuning safety

arXiv Machine Learning

Jul 22

A Better Start for Language Models: Domain-Conditional Position Offsets

arXiv:2607. 18302v1 Announce Type: new Abstract: Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior.

By Ye Qiao

llms rag fine-tuning safety

arXiv AI

Jul 22

LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation

arXiv:2602. 05493v2 Announce Type: replace-cross Abstract: Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks such as metaphor identification.

By Bingru Li

llms rag fine-tuning

arXiv AI

Jul 22

Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA

arXiv:2607. 18725v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation, remains difficult.

By Shaswata Mitra, Subash Neupane, Trisha Chakraborty, Himanshu Tripathi, Sudip Mittal, Aritran Piplai, Shahram Rahimi

llms fine-tuning safety

arXiv AI

Jul 22

Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

arXiv:2607. 18254v1 Announce Type: new Abstract: Multi-Level Intermediate Representation (MLIR) underlies modern ML compiler infrastructure (TensorFlow, JAX/StableHLO, PyTorch Inductor, IREE), yet appears only in trace amounts in code-LM pretraining corpora.

By Plawan Kumar Rath

fine-tuning benchmarks

arXiv Machine Learning

Jul 22

One Student, Many Teachers: Multi-Task On-Policy Distillation via Soft-Prompt Privileged Context

arXiv:2607. 18293v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) teaches large language models new skills through a teacher that shares the student's backbone and supervises its own rollouts.

By Yingzi Ma, Zichen Zhu, Ming Jiang, Chaowei Xiao

llms agents fine-tuning efficiency benchmarks

arXiv AI

Jul 22

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

arXiv:2607. 18361v1 Announce Type: cross Abstract: Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly labeled data and poor robustness to heterogeneous devices, placements, and users.

By Yuyang Leng (Richard), Renyuan Liu (Richard), Shaohan Hu (Richard), Peijun Zhao (Richard), Chun-Fu Chen (Richard), Songqing Chen, Shuochao Yao

diffusion fine-tuning benchmarks

arXiv AI

Jul 22

Parameter-Efficient Continual Fine-Tuning: A Survey

arXiv:2504. 13822v3 Announce Type: replace-cross Abstract: The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented performance.

By Eric Nuertey Coleman, Luigi Quarantiello, Ziyue Liu, Qinwen Yang, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco

fine-tuning benchmarks

arXiv AI

Jul 22

Structured Synthetic Reasoning Data for Arithmetic Fine-Tuning of Small Language Models

arXiv:2607. 18266v1 Announce Type: new Abstract: Small language models are attractive for local deployment, but they often struggle with multi-step arithmetic reasoning.

By Jake O'Grady, Effirul Ramlan

llms fine-tuning benchmarks

arXiv AI

Jul 22

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

arXiv:2607. 19064v1 Announce Type: cross Abstract: Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy.

By Xinjie Zhang, Peng Zhang, Shicheng Zheng, Jinghao Guo, Zhaoyang Jia, Yifei Shen, Xun Guo, Yuxuan Luo, Jiahao Li, Wenxuan Xie, Fanyi Pu, Xiaoyi Zhang, Kaichen Zhang, Zongyu Guo, Tianci Bi, Dongnan Gui, Zhening Liu, Zimo Wen, Zihan Zheng, Senqiao Yang, Xiao Li, Jinglu Wang, Bin Li, Yan Lu

llms diffusion nlp reinforcement-learning fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 22

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interactio

arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.

By Jialian Li, Junhong Liu, Yuchen Cao, Weiran Guo, Jiaming Song, Xutao Wang, Yi Zhao, Jiangpin Liu, Jie Chen

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 22

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

arXiv:2607. 18960v1 Announce Type: cross Abstract: Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth acquiring.

By Arther Tian, Alex Ding, Simon Wu, Aaron Chan

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 22

NSMA: Neuro-Symbolic Manifold Alignment for Generalizable Adaptive Bitrate Streaming under Texture Shift

arXiv:2607. 18845v1 Announce Type: cross Abstract: For decades, ABR has kept two kinds of intelligence apart.

By Zhiqiang He, Zhi Liu

diffusion fine-tuning benchmarks safety

arXiv AI

Jul 22

Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs

arXiv:2607. 18638v1 Announce Type: cross Abstract: Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstructed radiographs (DRRs) generated from diagnostic CT.

By Tomohiro Kikuchi, Kohei Yamamoto, Yukihiro Nomura, Yosuke Yamagishi, Takeharu Yoshikawa, Toshiaki Akashi, Jun Kamohara, Hiroyuki Fujii, Harushi Mori

fine-tuning

arXiv AI

Jul 22

Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary

arXiv:2607. 18553v1 Announce Type: cross Abstract: Can a language model read the quality of ongoing computation, and can an external intervention turn that readout into better outcomes?

By Jan Kirin

llms fine-tuning

arXiv Machine Learning

Jul 22

Recti-Q: Feature-Space Rectification for Out-of-Distribution-Robust Quantized Perception in Edge Robotics

arXiv:2607. 18540v1 Announce Type: cross Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-time inference.

By Hamidreza Yaghoubi Araghi, Parastoo Pilevar, Ming C. Lin

llms robotics fine-tuning efficiency benchmarks

arXiv AI

Jul 22

Soft-TransFormers for Continual Learning

arXiv:2411. 16073v4 Announce Type: replace-cross Abstract: Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework that adapts a frozen pre-trained Transformer through task-specific soft subnetworks: real-valued multiplicative masks over the query, key, value, and output projections of selected self-attention layers.

By Haeyong Kang, Chang D. Yoo

llms fine-tuning benchmarks

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