Tootfinder

Atlanta-based Rainforest, which lets platforms embed payment processing, raised a $29M Series B led by Matrix and Infinity, taking its total funding to $57.5M (Crunchbase News)
https://news.crunchbase.com/fintech/stripe-challenger-rainforest-seriesb-funding/…

Exclusive: Stripe Challenger Rainforest Lands $29M Series B
Rainforest, a startup that embeds payment processing into other software platforms, has raised $29 million in a Series B funding round, the company told Crunchbase News exclusively.

Oil exploration in the Congo basin rainforest could be a disaster for nature and the climate https://www.theguardian.com/environment/2025/aug/07/opening-up-the-congo-basin-rainforest-could-be-a-disaster-for-na…

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…

Die Stimme von "Houston, we´ve had a problem" ist verstummt
Mit 97 Jahren ist NASA-Astronaut Jim Lovell gestorben. Seine Raumfahrt-Karriere ist eine Geschichte des Scheiterns und Weitermachens – und von viel Glück.

TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
Haechang Kim, Hao Chen, Can Li, Jong Min Lee
https://arxiv.org/abs/2509.04809

TalkToAgent: A Human-centric Explanation of Reinforcement Learning Agents with Large Language Models
Explainable Reinforcement Learning (XRL) has emerged as a promising approach in improving the transparency of Reinforcement Learning (RL) agents. However, there remains a gap between complex RL policies and domain experts, due to the limited comprehensibility of XRL results and isolated coverage of current XRL approaches that leave users uncertain about which tools to employ. To address these challenges, we introduce TalkToAgent, a multi-agent Large Language Models (LLM) framework that delivers…

Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies
Yuhang Zhang, Jiaping Xiao, Chao Yan, Mir Feroskhan
https://arxiv.org/abs/2510.05692 https://

Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies
A prevailing approach for learning visuomotor policies is to employ reinforcement learning to map high-dimensional visual observations directly to action commands. However, the combination of high-dimensional visual inputs and agile maneuver outputs leads to long-standing challenges, including low sample efficiency and significant sim-to-real gaps. To address these issues, we propose Oracle-Guided Masked Contrastive Reinforcement Learning (OMC-RL), a novel framework designed to improve the samp…

Congressional Democrats are introducing a bill that would provide childcare relief to federal workers affected by the shutdown of the federal government,
as it drags on through a second week.
Parents would be reimbursed for fees paid to childcare facilities during the shutdown under plans initiated by Congresswoman Ilhan Omar.

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Wonseo Jang, Dongjae Kim
https://arxiv.org/abs/2509.04815 https://

An Arbitration Control for an Ensemble of Diversified DQN variants in Continual Reinforcement Learning
Deep reinforcement learning (RL) models, despite their efficiency in learning an optimal policy in static environments, easily loses previously learned knowledge (i.e., catastrophic forgetting). It leads RL models to poor performance in continual reinforcement learning (CRL) scenarios. To address this, we present an arbitration control mechanism over an ensemble of RL agents. It is motivated by and closely aligned with how humans make decisions in a CRL context using an arbitration control of m…

Multi-Task Reinforcement Learning with Language-Encoded Gated Policy Networks
Rushiv Arora
https://arxiv.org/abs/2510.06138 https://arxiv.org/pdf/2510.0613…

Multi-Task Reinforcement Learning with Language-Encoded Gated Policy Networks
Multi-task reinforcement learning often relies on task metadata -- such as brief natural-language descriptions -- to guide behavior across diverse objectives. We present Lexical Policy Networks (LEXPOL), a language-conditioned mixture-of-policies architecture for multi-task RL. LEXPOL encodes task metadata with a text encoder and uses a learned gating module to select or blend among multiple sub-policies, enabling end-to-end training across tasks. On MetaWorld benchmarks, LEXPOL matches or exce…

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
Jason Gardner, Ayan Dutta, Swapnoneel Roy, O. Patrick Kreidl, Ladislau Boloni
https://arxiv.org/abs/2509.05273

Greener Deep Reinforcement Learning: Analysis of Energy and Carbon Efficiency Across Atari Benchmarks
The growing computational demands of deep reinforcement learning (DRL) have raised concerns about the environmental and economic costs of training large-scale models. While algorithmic efficiency in terms of learning performance has been extensively studied, the energy requirements, greenhouse gas emissions, and monetary costs of DRL algorithms remain largely unexplored. In this work, we present a systematic benchmarking study of the energy consumption of seven state-of-the-art DRL algorithms, …