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 Machine Learning

Jun 4

Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform

arXiv:2603. 07523v3 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coupled with monolithic architecture, which restricts flexible reuse across models of varying scales.

By Jianlu Shen, Fu Feng, Yucheng Xie, Jiaqi Lv, Xin Geng

fine-tuning benchmarks

arXiv AI

Jun 4

Scaling Self-Evolving Agents via Parametric Memory

arXiv:2606. 04536v1 Announce Type: new Abstract: Existing memory-augmented LLM agents store past experience exclusively in prompt space, as textual summaries or retrieved passages, while keeping model parameters frozen throughout a rollout.

By Tao Ren, Weiyao Luo, Hui Yang, Rongzhi Zhu, Xiang Huang, Yuchuan Wu, Bingxue Chou, Jieping Ye, Jiafeng Liang, Yongbin Li, Yijie Peng

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 4

Symbolic Regression for Shared Expressions: Introducing Partial Parameter Sharing

arXiv:2601. 04051v3 Announce Type: replace Abstract: Symbolic regression aims to find symbolic expressions that describe datasets.

By Viktor Martinek, Roland Herzog

fine-tuning safety

arXiv Machine Learning

Jun 4

RL Excursions during Pre-Training: Re-examining Policy Optimization for LLM training

arXiv:2606. 04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT).

By Rachit Bansal, Clara Mohri, Tian Qin, David Alvarez-Melis, Sham Kakade

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 4

Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials

arXiv:2606. 04100v1 Announce Type: new Abstract: Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data.

By Joanna Zou, Fraser Birks, Dallas Foster, Youssef Marzouk

fine-tuning

arXiv AI

Jun 4

Geometry-Aware Hallucination Detection in Large Language Models

arXiv:2601. 06196v3 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate factually incorrect or unsupported content, commonly referred to as hallucinations.

By Bodla Krishna Vamshi, Rohan Bhatnagar, Haizhao Yang

llms rag nlp fine-tuning benchmarks safety

arXiv Machine Learning

Jun 4

Stateful Visual Encoders for Vision-Language Models

arXiv:2606. 04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes.

By Zirui Wang, Junwei Yu, Adam Yala, David M. Chan, Joseph E. Gonzalez, Trevor Darrell

llms agents fine-tuning multimodal

arXiv Machine Learning

Jun 4

MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

arXiv:2606. 04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code.

By Kun Cheng, Songshuo Lu, Sicong Liao, Tankun Li, Yafei Zhang, Dong Yang, Qiheng Lv, Hua Wang, Zhi Chen, Yaohua Tang

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 4

Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization

arXiv:2606. 04797v1 Announce Type: cross Abstract: Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts.

By Jiahua Dong, Wenqi Liang, Hongliu Li, Yang Cong, Duzhen Zhang, Hanbin Zhao, Henghui Ding, Yulun Zhang, Salman Khan, Fahad Shahbaz Khan

diffusion fine-tuning safety

arXiv AI

Jun 4

Overview of the EReL@MIR 2025 Multimodal Document Retrieval Challenge (Track 1)

arXiv:2606. 04240v1 Announce Type: cross Abstract: Retrieval over visually-rich documents, pages that interleave text with figures, tables, and charts, is essential for multimodal retrieval-augmented generation, yet most retrievers still discard the visual channel.

By Jingbiao Mei

llms rag fine-tuning multimodal

arXiv AI

Jun 4

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction

arXiv:2602. 23312v3 Announce Type: replace-cross Abstract: Leader-follower interaction is an important paradigm in human-robot interaction (HRI).

By Rafael R. Baptista, Andr\'e de Lima Salgado, Ricardo V. Godoy, Marcelo Becker, Thiago Boaventura, Gustavo J. G. Lahr

llms robotics fine-tuning benchmarks

arXiv AI

Jun 4

R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search

arXiv:2606. 04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizons, fluency does not ensure reliable delivery.

By Jo\~ao Pedro Gandarela, Thiago Rios, Stefan Menzel, Andr\'e Freitas

llms agents robotics fine-tuning safety

arXiv Machine Learning

Jun 4

An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers

arXiv:2606. 05149v1 Announce Type: cross Abstract: Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature.

By Gandhimathi Padmanaban, Fred Feng

llms computer-vision fine-tuning benchmarks safety

arXiv AI

Jun 4

MIRAGE: Mobile Agents with Implicit Reasoning and Generative World Models

arXiv:2606. 04627v1 Announce Type: new Abstract: Mobile agents are increasingly expected to operate everyday applications from screenshots and language goals, where reliable control requires reasoning over screen affordances, multi-step navigation, and future state changes.

By Zhichao Yang, Yuanze Hu, Haojie Hao, Longkun Hao, Dongshuo Huang, Hongyu Lin, Gen Li, Lanqing Hong, Yihang Lou, Yan Bai

agents diffusion fine-tuning benchmarks

arXiv AI

Jun 4

EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms

arXiv:2606. 04145v1 Announce Type: cross Abstract: Cloud LLM fine-tuning platforms increasingly serve RLHF workloads, where a learned reward model is optimized as a proxy for human quality.

By Guilin Zhang, Chuanyi Sun, Shahryar Sarkani, John M. Fossaceca

llms reinforcement-learning fine-tuning

arXiv AI

Jun 4

BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization

arXiv:2606. 04807v1 Announce Type: new Abstract: Mitigating social bias in Large Language Models (LLMs) presents a distinct alignment challenge: unlike verifiable tasks, bias lacks a single ground truth, creating a high-variance, subjective reward landscape.

By Saket Reddy, Ke Yang, ChengXiang Zhai

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 4

Vision Transformer Finetuning Benefits from Non-Smooth Components

arXiv:2602. 06883v3 Announce Type: replace Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness.

By Ambroise Odonnat, Laetitia Chapel, Romain Tavenard, Ievgen Redko

llms computer-vision fine-tuning safety

arXiv AI

Jun 4

Building The Ph(ysical)AI Layer Of Machine Intelligence

arXiv:2606. 04106v1 Announce Type: cross Abstract: Foundation models achieve generalization through massive-scale training on diverse data, but have limitations with transfer to truly unseen domains without paired training data.

By Ulbert Jose Botero, Liam Smith, Brooks Olney, Pooya Khorrami, Steven Kusiak, Watson Jia, Sage Trudeau, Daniel Capecci

fine-tuning multimodal

arXiv AI

Jun 4

Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation

arXiv:2606. 04269v1 Announce Type: cross Abstract: Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes.

By Yilong Wang, Cheng Qian, Edward Johns

llms robotics fine-tuning

arXiv AI

Jun 4

StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis

arXiv:2606. 04246v1 Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL.

By Prashanth Vijayaraghavan, Apoorva Nitsure, Luyao Shi, Ehsan Degan, Vandana Mukherjee

llms rag reinforcement-learning fine-tuning benchmarks

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