Tootfinder

I genuinely think whoever designed man city's third kit should go to prison for the rest of their life #fedifc https://mastodon.social/@footiebuzz/115453337798882675

Language Models Can Learn from Verbal Feedback Without Scalar Rewards
Renjie Luo, Zichen Liu, Xiangyan Liu, Chao Du, Min Lin, Wenhu Chen, Wei Lu, Tianyu Pang
https://arxiv.org/abs/2509.22638

Language Models Can Learn from Verbal Feedback Without Scalar Rewards
LLMs are often trained with RL from human or AI feedback, yet such methods typically compress nuanced feedback into scalar rewards, discarding much of their richness and inducing scale imbalance. We propose treating verbal feedback as a conditioning signal. Inspired by language priors in text-to-image generation, which enable novel outputs from unseen prompts, we introduce the feedback-conditional policy (FCP). FCP learns directly from response-feedback pairs, approximating the feedback-conditi…

Student Engagement with GenAI's Tutoring Feedback: A Mixed Methods Study
Sven Jacobs, Jan Haas, Natalie Kiesler
https://arxiv.org/abs/2509.22974 https://

Student Engagement with GenAI's Tutoring Feedback: A Mixed Methods Study
How students utilize immediate tutoring feedback in programming education depends on various factors. Among them are the feedback quality, but also students' engagement, i.e., their perception, interpretation, and use of feedback. However, there is limited research on how students engage with various types of tutoring feedback. For this reason, we developed a learning environment that provides students with Python programming tasks and various types of immediate, AI-generated tutoring feedback.…

The secret big sad doesn't want us to know.
🎨by artofdanialryan

I thought this had a very different name on it at first.

De VVD heeft kiezers verloren aan CDA en D66, maar kiezers gewonnen van PVV en NSC.

Learning from Delayed Feedback in Games via Extra Prediction
Yuma Fujimoto, Kenshi Abe, Kaito Ariu
https://arxiv.org/abs/2509.22426 https://arxiv.org/pdf/2…

Learning from Delayed Feedback in Games via Extra Prediction
This study raises and addresses the problem of time-delayed feedback in learning in games. Because learning in games assumes that multiple agents independently learn their strategies, a discrepancy in optimization often emerges among the agents. To overcome this discrepancy, the prediction of the future reward is incorporated into algorithms, typically known as Optimistic Follow-the-Regularized-Leader (OFTRL). However, the time delay in observing the past rewards hinders the prediction. Indeed,…

Autofreie Togostraße-
Bauvorbereitung für die Fahrradstraße

WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
Zimu Lu, Houxing Ren, Yunqiao Yang, Ke Wang, Zhuofan Zong, Junting Pan, Mingjie Zhan, Hongsheng Li
https://arxiv.org/abs/2509.22644

WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
Agent systems powered by large language models (LLMs) have demonstrated impressive performance on repository-level code-generation tasks. However, for tasks such as website codebase generation, which depend heavily on visual effects and user-interaction feedback, current code agents rely only on simple code execution for feedback and verification. This approach fails to capture the actual quality of the generated code. In this paper, we propose WebGen-Agent, a novel website-generation agent tha…

Automated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis
Tiago Fernandes Tavares, Luciano Pereira Soares
https://arxiv.org/abs/2509.22734

Automated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis
This paper explores the development and adoption of AI-based formative feedback in the context of biweekly reports in an engineering Capstone program. Each student is required to write a short report detailing their individual accomplishments over the past two weeks, which is then assessed by their advising professor. An LLM-powered tool was developed to provide students with personalized feedback on their draft reports, guiding them toward improved completeness and quality. Usage data across t…