Tootfinder

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
George Frideric Handel, Yvonne Kenny, Melbourne Symphony Orchestra & Vladimir Kamirski:
🎵 Rejoice greatly, O daughter of Zion' (aria from "The Messiah")
#YvonneKenny
https://open.spotify.com/track/0WlEm0beD7w2tZx8tey5Ui

🇺🇦 #NowPlaying on KEXP's #SoundsOfSurvivance
Ampersan:
🎵 Dolorosa
#Ampersan
https://ampersan.bandcamp.com/track/dolorosa
https://open.spotify.com/track/4YfNSSOFDQbyLWXMI8USc8

Towards a Unified View of Large Language Model Post-Training
Xingtai Lv, Yuxin Zuo, Youbang Sun, Hongyi Liu, Yuntian Wei, Zhekai Chen, Lixuan He, Xuekai Zhu, Kaiyan Zhang, Bingning Wang, Ning Ding, Bowen Zhou
https://arxiv.org/abs/2509.04419

Towards a Unified View of Large Language Model Post-Training
Two major sources of training data exist for post-training modern language models: online (model-generated rollouts) data, and offline (human or other-model demonstrations) data. These two types of data are typically used by approaches like Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT), respectively. In this paper, we show that these approaches are not in contradiction, but are instances of a single optimization process. We derive a Unified Policy Gradient Estimator, and present …

Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Models
Tianren Ma, Mu Zhang, Yibing Wang, Qixiang Ye
https://arxiv.org/abs/2510.02880 https://

Consolidating Reinforcement Learning for Multimodal Discrete Diffusion Models
Optimizing discrete diffusion model (DDM) with rewards remains a challenge: the non-autoregressive paradigm makes importance sampling intractable and rollout complex, puzzling reinforcement learning methods such as Group Relative Policy Optimization (GRPO). In this study, we introduce MaskGRPO, the first viable approach to enable scalable multimodal reinforcement learning in discrete diffusion with effective importance sampling and modality-specific adaptations. To this end, we first clarify th…

MTQA:Matrix of Thought for Enhanced Reasoning in Complex Question Answering
Fengxiao Tang, Yufeng Li, Zongzong Wu, Ming Zhao
https://arxiv.org/abs/2509.03918 https://

MTQA:Matrix of Thought for Enhanced Reasoning in Complex Question Answering
Complex Question Answering (QA) is a fundamental and challenging task in NLP. While large language models (LLMs) exhibit impressive performance in QA, they suffer from significant performance degradation when facing complex and abstract QA tasks due to insufficient reasoning capabilities. Works such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT) aim to enhance LLMs' reasoning abilities, but they face issues such as in-layer redundancy in tree structures and single paths in chain structures…

A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
Mingfeng Fan, Jiaqi Cheng, Yaoxin Wu, Yifeng Zhang, Yibin Yang, Guohua Wu, Guillaume Sartoretti
https://arxiv.org/abs/2510.03065

A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
In recent years, deep reinforcement learning (DRL) has gained traction for solving the NP-hard traveling salesman problem (TSP). However, limited attention has been given to the close-enough TSP (CETSP), primarily due to the challenge introduced by its neighborhood-based visitation criterion, wherein a node is considered visited if the agent enters a compact neighborhood around it. In this work, we formulate a Markov decision process (MDP) for CETSP using a discretization scheme and propose a n…

🇺🇦 #NowPlaying on BBCRadio3's #NightTracks
Laurie Spiegel & Laurie Spiegel:
🎵 Appalachian Grove I
#LaurieSpiegel
https://open.spotify.com/track/58ODorNYopmxNhZnWdjQjO

T2UE: Generating Unlearnable Examples from Text Descriptions
Xingjun Ma, Hanxun Huang, Tianwei Song, Ye Sun, Yifeng Gao, Yu-Gang Jiang
https://arxiv.org/abs/2508.03091 https://

T2UE: Generating Unlearnable Examples from Text Descriptions
Large-scale pre-training frameworks like CLIP have revolutionized multimodal learning, but their reliance on web-scraped datasets, frequently containing private user data, raises serious concerns about misuse. Unlearnable Examples (UEs) have emerged as a promising countermeasure against unauthorized model training, employing carefully crafted unlearnable noise to disrupt the learning of meaningful representations from protected data. Current approaches typically generate UEs by jointly optimizi…

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Rui Qi, Zhibo Man, Yufeng Chen, Fengran Mo, Jinan Xu, Kaiyu Huang
https://arxiv.org/abs/2510.02648

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successfully transferred to non-high-resource languages due to resource constraints, which struggles with multilingual reasoning tasks. To this end, we propose Structured-of-Thought (SoT), a training-free method that improves the performance on multilingual reasoning through a multi-step transformation: Language Thinking Transf…

Self-Improvement in Multimodal Large Language Models: A Survey
Shijian Deng, Kai Wang, Tianyu Yang, Harsh Singh, Yapeng Tian
https://arxiv.org/abs/2510.02665 https://

Self-Improvement in Multimodal Large Language Models: A Survey
Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). …