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 4

SePO: Self-Evolving Prompt Agent for System Prompt Optimization

arXiv:2606. 04465v1 Announce Type: cross Abstract: System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions.

By Wangcheng Tao, Han Wu, Weng-Fai Wong

agents fine-tuning benchmarks

arXiv Machine Learning

Jun 4

Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

arXiv:2606. 04314v1 Announce Type: new Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability.

By Bin Duan, Meiru Che, Guowei Yang

fine-tuning safety

arXiv Machine Learning

Jun 4

FoeGlass: Simple In-Context Learning Is Enough for Red Teaming Audio Deepfake Detectors

arXiv:2606. 05101v1 Announce Type: cross Abstract: Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models.

By Sepehr Dehdashtian, Jacob H Seidman, Vishnu N Boddeti, Gaurav Bharaj

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 4

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning

arXiv:2605. 23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data.

By Trinh Pham, Viet Huynh, Hongzhi Yin, Quoc Viet Hung Nguyen, Thanh Tam Nguyen

fine-tuning benchmarks

arXiv AI

Jun 4

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

arXiv:2606. 04231v1 Announce Type: cross Abstract: Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation.

By Hanoz Bhathena, Parin Rajesh Jhaveri, Rohan Mittal, Prateek Singh, Aymen Kallala, Rachneet Kaur, Yiqiao Jin, Zhen Zeng, Adwait Ratnaparkhi, Denis Kochedykov

llms rag fine-tuning multimodal benchmarks safety

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

arXiv AI

Jun 4

Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning

arXiv:2505. 17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored.

By Wang Yang, Zirui Liu, Hongye Jin, Qingyu Yin, Vipin Chaudhary, Xiaotian Han

llms fine-tuning benchmarks

arXiv AI

Jun 4

The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail

arXiv:2606. 04010v1 Announce Type: cross Abstract: Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data.

By Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell, Victoria Shevchenko, Demian Wassermann

llms fine-tuning benchmarks

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 AI

Jun 4

The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids

arXiv:2606. 04103v1 Announce Type: cross Abstract: Conventional hearing aids rely on fixed, frequency-dependent amplification and compression to manage reduced sensitivity, which often fails to provide sufficient listening support in complex environments, such as situations with multiple speakers (the ``cocktail party'' problem).

By Alejandro Ballesta Rosen, Jason Mikiel-Hunter, Julian Maclaren, Jack Collins, Richard F. Lyon, Simon Carlile

fine-tuning

arXiv AI

Jun 4

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606. 04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typically incurs severe accuracy degradation.

By Devleena Das, Rajeev Patwari, Elliott Delaye, Ashish Sirasao

llms fine-tuning efficiency benchmarks

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 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

ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models

arXiv:2605. 18879v3 Announce Type: replace-cross Abstract: Large language models inevitably retain sensitive information, defined as inputs that may induce harmful generations, due to training on massive web corpora, raising concerns for privacy and safety.

By Yujie Lin, Chengyi Yang, Zhishang Xiang, Yiping Song, Jinsong Su

llms fine-tuning safety

arXiv Machine Learning

Jun 4

Efficient Reasoning on the Edge

arXiv:2603. 16867v2 Announce Type: replace Abstract: Large language models (LLMs) with chain-of-thought reasoning achieve state-of-the-art performance across complex problem-solving tasks, but their verbose reasoning traces and large context requirements make them impractical for edge deployment.

By Yelysei Bondarenko, Thomas Hehn, Rob Hesselink, Romain Lepert, Fabio Valerio Massoli, Evgeny Mironov, Leyla Mirvakhabova, Tribhuvanesh Orekondy, Spyridon Stasis, Andrey Kuzmin, Anna Kuzina, Markus Nagel, Ankita Nayak, Corrado Rainone, Ork de Rooij, Paul N Whatmough, Arash Behboodi, Babak Ehteshami Bejnordi

llms reinforcement-learning fine-tuning efficiency benchmarks

arXiv Machine Learning

Jun 4

Reasoning Shift: How Context Silently Shortens LLM Reasoning

arXiv:2604. 01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remarkable performance on complex, long-term reasoning tasks.

By Gleb Rodionov, Roman Garipov, George Yakushev

llms agents fine-tuning

arXiv AI

Jun 4

TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering

arXiv:2602. 06911v2 Announce Type: replace-cross Abstract: As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications, whether accidental or intentional, becomes critical to minimize risks.

By Saad Hossain, Tom Tseng, Punya Syon Pandey, Samanvay Vajpayee, Matthew Kowal, Nayeema Nonta, Samuel Simko, Stephen Casper, Zhijing Jin, Kellin Pelrine, Sirisha Rambhatla

llms fine-tuning benchmarks safety

arXiv AI

Jun 4

Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers

arXiv:2601. 07036v2 Announce Type: replace-cross Abstract: Hybrid reasoning language models are commonly controlled through high-level Think/No-think instructions to regulate reasoning behavior, yet we found that such mode switching is largely driven by a small set of trigger tokens rather than the instructions themselves.

By Wang Yang, Debargha Ganguly, Xinpeng Li, Chaoda Song, Shouren Wang, Vikash Singh, Vipin Chaudhary, Xiaotian Han

llms reinforcement-learning fine-tuning

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

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

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