arXiv Machine Learning July 1, 2026 By Minchan Kwon, Sunghyun Baek, Minseo Kim, Jaemyung Yu, Dongyoon Han, Junmo Kim

Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance

Read the original on arXiv Machine Learning →

arXiv:2605. 00553v3 Announce Type: replace Abstract: Large Language Model (LLM) Red-Teaming, which proactively identifies vulnerabilities of LLMs, is an essential process for ensuring safety.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 16

GFlowRL: Scaling Distribution-Matching RL to Large Language Models

arXiv:2607. 13394v1 Announce Type: cross Abstract: Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse reasoning paths by matching reward distributions rather than collapsing to dominant modes.

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llms reinforcement-learning multimodal benchmarks safety

arXiv Machine Learning

Jul 30

GPT-Red: Automated Red Teaming via Self-Play at Scale

arXiv:2607. 26115v1 Announce Type: cross Abstract: We introduce \textbf{GPT-Red}, an automated red-teaming agent that is trained to discover novel prompt injection attacks against frontier LLMs.

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llms agents reinforcement-learning safety

arXiv AI

Aug 12

Generating Attacks for LLMs with GFlowNets

arXiv:2608. 10171v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has facilitated their ubiquitous integration into various domains, leading to widespread adoption.

By Berkay Ozcam, Irem Onen, Mehmet Fatih Amasyali, Emin Islam Tatli

llms benchmarks safety

arXiv AI

Jun 9

Learning to Attack and Defend: Adaptive Red Teaming of Language Models via GRPO

arXiv:2606. 09701v1 Announce Type: cross Abstract: AI red teaming must continually adapt to evolving attackers and defenders.

By Blake Bullwinkel, Eugenia Kim, Amanda Minnich, Mark Russinovich

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 4

Quality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models

arXiv:2506. 07121v2 Announce Type: replace Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deployment of artificial intelligence.

By Ren-Jian Wang, Ke Xue, Zeyu Qin, Ziniu Li, Sheng Tang, Hao-Tian Li, Shengcai Liu, Zhi Yu, Yuanpeng Tan, Chao Qian

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arXiv AI

Jul 8

RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations

arXiv:2607. 06109v1 Announce Type: cross Abstract: Multi-perturbation adversarial training (MAT) aims to achieve robustness against multiple $\ell_p$ perturbations but suffers from robustness trade-offs between different threats.

By Woo Jae Kim, Kyle Min, Suhyeon Ha, Joonsung Jeon, Sung-eui Yoon

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