Tootfinder

Wie sinnvoll sind Dusch-Wärmetauscher?
Derzeit sind Dusch-Wärmetauscher ein Thema in den Medien. Ob sie sich wirklich rechnen, lässt sich leicht nachprüfen.
https://www.heise.de/hintergrund/Wie-s…

'Pokrovsk is simply being absorbed' — Ukraine’s defense on a knife-edge: https://benborges.xyz/2025/11/06/pokrovsk-is-simply-being-absorbed.html

cell phones and tablets:
https://mastodon.social/@simple_sabotage/115504195285748004

"5 schnelle Sketchnotes-Symbole, die du im Dezember überall verwenden kannst (mit Anleitung)" #sketchnote

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Simple Minds:
🎵 Promised You a Miracle
#SimpleMinds
https://djaf.bandcamp.com/track/simple-minds-promised-you-a-miracle-redrum
https://open.spotify.com/track/2LTka22D6eVbB0ekwglXuy

The dangerous symbolism of the Trump coins - Salon.com
https://www.salon.com/2025/10/07/the-dangerous-symbolism-of-the-trump-coins/

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Markus Walker, Daniel Frisch, Uwe D. Hanebeck
https://arxiv.org/abs/2510.05706

Sample-Efficient and Smooth Cross-Entropy Method Model Predictive Control Using Deterministic Samples
Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance on random sampling. This conventional approach can lead to inefficient exploration of the solution space and non-smooth control inputs, requiring a large number of samples to achieve satisfactory results. To address these limitations, we propose deterministic sampling CEM (dsCEM), a novel framework that replaces the ra…

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu
https://arxiv.org/abs/2510.05023 https:…

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Most existing approximate Thompson Sampling (TS) algorithms for multi-armed bandits use Stochastic Gradient Langevin Dynamics (SGLD) or its variants in each round to sample from the posterior, relaxing the need for conjugacy assumptions between priors and reward distributions in vanilla TS. However, they often require approximating a different posterior distribution in different round of the bandit problem. This requires tricky, round-specific tuning of hyperparameters such as dynamic learning …

Symbolic Graphics Programming with Large Language Models
Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu
https://arxiv.org/abs/2509.05208

Symbolic Graphics Programming with Large Language Models
Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored. We study symbolic graphics programming, where the goal is to generate an SGP from a natural-language description. This task also serves as a lens into how LLMs understand the visual world by prompting them to generate images rendered from SGPs. Among various SGPs, our paper sticks to scalable vector graphics (SVGs)…

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian, Nan Jiang, Tong Zhang
https://arxiv.org/abs/2510.04996

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates due to fixed and uniform sampling of responses across prompts. Prior work such as GVM-RAFT addresses this by dynamically allocating inference budget per prompt to minimize stochastic gradient variance under a budget constraint. Inspired by this insight, we propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs that continuousl…