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

Jun 9

TLDR: Compressing Audio Tokens for Efficient Autoregressive Text-to-Speech

arXiv:2606. 09019v1 Announce Type: cross Abstract: Codec-based autoregressive (AR) speech language models have achieved strong text-to-speech (TTS) quality by modeling speech as sequences of discrete audio tokens with large pretrained backbones.

By Yejin Lee, Junwon Moon, Hyoeun Kim, Hyunjin Choi, Heeseung Kim, Kyuhong Shim

llms fine-tuning efficiency multimodal

arXiv Machine Learning

Jun 9

From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning

arXiv:2606. 09092v1 Announce Type: new Abstract: Theory of Mind (ToM) is a must-acquire skill for modern foundation model systems to operate effectively and safely in the real world.

By Jike Zhong, Yuxiang Lai, Ming Li, Yuheng Li, Wuao Liu, Behzad Dariush, Konstantinos Psounis, Shao-Yuan Lo

reinforcement-learning fine-tuning multimodal

arXiv AI

Jun 9

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models

arXiv:2606. 08446v1 Announce Type: cross Abstract: Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive.

By Yang Zhou, Ranajoy Sadhukhan, Zhaofeng Sun, Zhuoming Chen, Souvik Kundu, Saket Dingliwal, Sai Muralidhar Jayanthi, Aram Galstyan, Haizhong Zheng, Beidi Chen

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Jun 9

Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning

arXiv:2601. 04805v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have attracted much attention due to their exceptional performance.

By Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao

reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

LogNEO: A GPT-Neo Reinforcement Learning Framework for Accurate Real-Time Log Anomaly Detection

arXiv:2606. 08153v1 Announce Type: cross Abstract: Detecting anomalies in large-scale system logs is critical for the reliability and security of modern computing infrastructure.

By David Eje, Tanmay Sharma, Khush Patel, Manuel Mazzara, Leonard Johard

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 9

Muon Learns More Robust and Transferable Features than Adam

arXiv:2606. 09658v1 Announce Type: cross Abstract: Muon has recently emerged as a state-of-the-art optimizer for pretraining Large Language Models (LLMs) and vision classifiers.

By Tianyu Ruan, Fengzhuo Zhang, Shuche Wang, Shihua Zhang

llms fine-tuning benchmarks

arXiv AI

Jun 9

Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning

arXiv:2606. 09353v1 Announce Type: cross Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms.

By Maria De Marsico, Anil K. Jain, Annalaura Miglino

llms computer-vision fine-tuning benchmarks

arXiv AI

Jun 9

Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models

arXiv:2606. 09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexplored.

By Danya Li, Xiang Su, Yan Feng, Rico Krueger

llms nlp fine-tuning benchmarks safety

arXiv AI

Jun 9

ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies

arXiv:2606. 08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions.

By Bingjia Huang, Xiangyu Li, Xiang Wang, Liang Mi, Zixu Hao, Weijun Wang, Hao Wu, Kun Li, Yunxin Liu, Ting Cao

reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jun 9

Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline

arXiv:2606. 07965v1 Announce Type: new Abstract: Large Visual Language Models (LVLMs) have achieved remarkable success in vision tasks.

By Zekai Zhang, Qinghui Chen, Maomao Xiong, Shijiao Ding, Zhanzhi Su, Xinjie Yao, Yiming Sun, Cong Bai, Jinglin Zhang

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 9

AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning

arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.

By Jiarui Zhang, Junqi Hu, Zurong Mai, Yang Liu, Yuhang Chen, Shuohong Lou, Henglian Huang, Hong Cheng, Lingyuan Zhao, Jianxi Huang, Yutong Lu, Haohuan Fu, Juepeng Zheng

llms nlp fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jun 9

Foundation Inference Models for Ordinary Differential Equations

arXiv:2602. 08733v2 Announce Type: replace Abstract: Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challenging.

By Maximilian Mauel, Johannes R. H\"ubers, David Berghaus, Patrick Seifner, Ramses J. Sanchez

fine-tuning

arXiv AI

Jun 9

Principled Agent Debate: Adversarial Arbitration for Sycophancy Reduction in Large Language Models

arXiv:2606. 07532v1 Announce Type: cross Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process.

By Sam Ryan

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 9

A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationales

arXiv:2606. 09470v1 Announce Type: cross Abstract: Automated L2 speech assessment can assign proficiency labels, but often lacks interpretability.

By Aditya Kamlesh Parikh, Cristian Tejedor-Garcia, Catia Cucchiarini, Helmer Strik

fine-tuning safety

arXiv AI

Jun 9

Phantom transitions in language model fine-tuning

arXiv:2606. 07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently.

By Vaibhav Prakash, Jayasri Dontabhaktuni

llms rag fine-tuning

arXiv Machine Learning

Jun 9

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series

arXiv:2606. 07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle.

By Nick Teutschmann (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Laura Crocetti (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Fanny Lehmann (ETH AI Center, Switzerland), Leonardo Trentini (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland), Benedikt Soja (Institute of Geodesy and Photogrammetry, ETH Zurich, Switzerland)

fine-tuning efficiency

arXiv AI

Jun 9

Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound

arXiv:2502. 16584v2 Announce Type: replace-cross Abstract: Recent advancements in audio tokenization have significantly enhanced the integration of audio capabilities into large language models (LLMs).

By Liumeng Xue, Ziya Zhou, Jiahao Pan, Zixuan Li, Shuai Fan, Yinghao Ma, Sitong Cheng, Dongchao Yang, Haohan Guo, Yujia Xiao, Xinsheng Wang, Zixuan Shen, Chuanbo Zhu, Xinshen Zhang, Tianchi Liu, Ruibin Yuan, Zeyue Tian, Haohe Liu, Xingjian Du, Emmanouil Benetos, Ge Zhang, Yike Guo, Wei Xue

llms nlp fine-tuning

arXiv Machine Learning

Jun 9

Hierarchical Projection for Adaptive Knowledge Transfer

arXiv:2606. 08691v1 Announce Type: new Abstract: Modern data-driven applications increasingly involve learning from multiple heterogeneous sources, where a target dataset is limited but related information is available across domains.

By Samhita Pal, Tian Gu

fine-tuning safety

arXiv AI

Jun 9

Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings

arXiv:2509. 10534v3 Announce Type: replace-cross Abstract: The attention mechanism in a Transformer architecture matches key to query based on both content -- the what -- and position in a sequence -- the where.

By Anand Gopalakrishnan, Robert Csord\'as, J\"urgen Schmidhuber, Michael C. Mozer

llms rag fine-tuning

arXiv AI

Jun 9

ZIPP:Zero-shot Image Personalization from Personas

arXiv:2606. 08841v1 Announce Type: new Abstract: Text-to-image diffusion models are increasingly deployed in open-ended creative contexts, yet their outputs remain impersonal, optimized for aggregate aesthetics rather than individual taste.

By Harini SI, Somesh Singh, Yaman Kumar Singla, David Doermann, Rajiv Ratn Shah

llms diffusion fine-tuning benchmarks safety

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