Tootfinder

Rampart Talks
Exclusive interviews with the most intriguing businesspeople Australia has going for it...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/rampart-talks/

Fusion: EU-Satellitenbetreiber wollen gemeinsam Musks SpaceX Paroli bieten
Airbus, Leonardo und Thales planen den Zusammenschluss ihrer Raumfahrtsparten. Die EU-Wettbewerbsbehörden müssen den Deal noch prüfen, OHB erhebt Einspruch.

Global climate negotiations ended on Saturday in Brazil
with a watered-down resolution that makes no mention of fossil fuels, the main driver of global warming.
The final statement included plenty of warnings on the cost of inaction
but few provisions for how the world might address dangerously rising global temperatures head-on.
A marathon series of frenetic Friday night meetings ultimately salvaged the talks in Belém, on the edge of the Amazon rainforest.
The …

COP30 Climate Summit Ends With Dire Warnings and Scant Plans for Action
The final agreement, with no direct mention of the fossil fuels that are dangerously heating the planet, was a victory for oil-producing countries.

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools
Zhenlong Yuan, Xiangyan Qu, Chengxuan Qian, Rui Chen, Jing Tang, Lei Sun, Xiangxiang Chu, Dapeng Zhang, Yiwei Wang, Yujun Cai, Shuo Li
https://arxiv.org/abs/2510.08480

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools
Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text-centric priors often limits their ability to disentangle semantically similar actions in open-vocabulary scenarios. To address this, we propose Video-STAR, a framework that harmonizes contextual sub-motion decomposition with tool-augmented reinforcement learning for open-vocabulary action recognition (OVAR). Unlike prior methods that treat actions …

ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools
Quy Minh Le, Minh Sao Khue Luu, Khanh-Tung Tran, Duc-Hai Nguyen, Hoang-Quoc-Viet Pham, Quan Le, Hoang Thanh Lam, Hoang D. Nguyen
https://arxiv.org/abs/2510.00023

ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools
Effective tool use is essential for agentic AI, yet training agents to utilize tools remains challenging due to manually designed rewards, limited training data, and poor multi-tool selection, resulting in slow adaptation, wasted computational resources, and suboptimal performance. We introduce ToolBrain, a lightweight and user-friendly framework for coaching tool use in agentic models with flexible reinforcement learning (RL), easing the barriers for researchers and practitioners to adapt LLM-…

"For Afghan women, the stakes could not be any clearer. Every gesture towards normalization tells them their rights can be traded away for political convenience. Every meeting that sidelines the issue of gender apartheid reinforces the message that their lives are secondary to geopolitical interests. "
UN Report Details Deepening Crisis for Afghan Women and Warns Against Normalising Taliban Rule - Feminist Majority Foundation
https://feminist.org/news/un-report-details-deepening-crisis-for-afghan-women-and-warns-against-normalising-taliban-rule/

Adaptive Tool Generation with Models as Tools and Reinforcement Learning
Chenpeng Wang, Xiaojie Cheng, Chunye Wang, Linfeng Yang, Lei Zhang
https://arxiv.org/abs/2510.06825 http…

Adaptive Tool Generation with Models as Tools and Reinforcement Learning
Tool-augmented language models have demonstrated strong capabilities, but their reliance on live API access creates scalability and reliability challenges during training and deployment. We propose MTR, a simulation-first training framework for tool-augmented reasoning. Instead of relying on live APIs, MTR learns from complete ReAct traces with schema-validated, simulated observations. Our approach operates through a multi-agent architecture where a ToolMaker generates task-specific, OpenAI-com…

Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2510.06214

Stratified GRPO: Handling Structural Heterogeneity in Reinforcement Learning of LLM Search Agents
Large language model (LLM) agents increasingly rely on external tools such as search engines to solve complex, multi-step problems, and reinforcement learning (RL) has become a key paradigm for training them. However, the trajectories of search agents are structurally heterogeneous, where variations in the number, placement, and outcomes of search calls lead to fundamentally different answer directions and reward distributions. Standard policy gradient methods, which use a single global baselin…

Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning
Wenxun Wu, Yuanyang Li, Guhan Chen, Linyue Wang, Hongyang Chen
https://arxiv.org/abs/2510.07038

Tool-Augmented Policy Optimization: Synergizing Reasoning and Adaptive Tool Use with Reinforcement Learning
Recent advances in large language models (LLMs) have popularized test-time scaling, where models generate additional reasoning tokens before producing final answers. These approaches have demonstrated significant performance improvements on benchmarks involving mathematical reasoning. However, language models relying solely on direct inference still struggle with tasks demanding up-to-date knowledge or computational tools such as calculators and code interpreters for complex arithmetic operatio…

Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Jens Tuyls, Dylan J. Foster, Akshay Krishnamurthy, Jordan T. Ash
https://arxiv.org/abs/2510.11686

Representation-Based Exploration for Language Models: From Test-Time to Post-Training
Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the discovery of novel behaviors, or simply sharpen those already present in the base model. In this paper, we investigate the value of deliberate exploration -- explicitly incentivizing the model to discover novel and diverse behaviors -- and aim to understand how the knowledge in pre-trained models can guide this search. Our main finding is that exploration wi…