Tootfinder

from my link log —
The ITTAGE indirect branch predictor.
https://blog.nelhage.com/post/ittage-branch-predictor/
saved 2025-07-05

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
Georg Kruse, Rodrigo Coelho, Andreas Rosskopf, Robert Wille, Jeanette Miriam Lorenz
https://arxiv.org/abs/2507.07593

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
At the interception between quantum computing and machine learning, Quantum Reinforcement Learning (QRL) has emerged as a promising research field. Due to its novelty, a standardized and comprehensive collection for QRL algorithms has not yet been established. Researchers rely on numerous software stacks for classical Reinforcement Learning (RL) as well as on various quantum computing frameworks for the implementation of the quantum subroutines of their QRL algorithms. Inspired by the CleanRL l…

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Siyi Lu, Run Liu, Dongsheng Yang, Lei He
https://arxiv.org/abs/2508.06074

ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
Autonomous driving systems face significant challenges in perceiving complex environments and making real-time decisions. Traditional modular approaches, while offering interpretability, suffer from error propagation and coordination issues, whereas end-to-end learning systems can simplify the design but face computational bottlenecks. This paper presents a novel approach to autonomous driving using deep reinforcement learning (DRL) that integrates bird's-eye view (BEV) perception for enhanced …

This https://arxiv.org/abs/2505.22942 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning
Large language models (LLMs)-empowered web agents enables automating complex, real-time web navigation tasks in enterprise environments. However, existing web agents relying on supervised fine-tuning (SFT) often struggle with generalization and robustness due to insufficient reasoning capabilities when handling the inherently dynamic nature of web interactions. In this study, we introduce WorkForceAgent-R1, an LLM-based web agent trained using a rule-based R1-style reinforcement learning framew…

This https://arxiv.org/abs/2506.05069 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
Driven by advances in Large Language Models (LLMs), integrating them into recommendation tasks has gained interest due to their strong semantic understanding and prompt flexibility. Prior work encoded user-item interactions or metadata into prompts for recommendations. In parallel, LLM reasoning, boosted by test-time scaling and reinforcement learning, has excelled in fields like mathematics and code, where reasoning traces and correctness signals are clear, enabling high performance and interp…

Reinforcement Learning for Trade Execution with Market Impact
Patrick Cheridito, Moritz Weiss
https://arxiv.org/abs/2507.06345 https://

Reinforcement Learning for Trade Execution with Market Impact
In this paper, we introduce a novel reinforcement learning framework for optimal trade execution in a limit order book. We formulate the trade execution problem as a dynamic allocation task whose objective is the optimal placement of market and limit orders to maximize expected revenue. By employing multivariate logistic-normal distributions to model random allocations, the framework enables efficient training of the reinforcement learning algorithm. Numerical experiments show that the proposed…

Predictive reinforcement learning based adaptive PID controller
Chaoqun Ma, Zhiyong Zhang
https://arxiv.org/abs/2506.08509 https://ar…

Predictive reinforcement learning based adaptive PID controller
Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines the advantages of both data-driven and model-driven approaches. Design/methodology/approach: A predictive reinforcement learning framework is introduced, incorporating action smooth strategy to suppress overshoot and oscillations, and a hierarchical reward function to support trai…

Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
Jerome Brender, Laila El-Hamamsy, Kim Uittenhove, Francesco Mondada, Engin Bumbacher
https://arxiv.org/abs/2507.07767

Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
Prior research shows that how students engage with Large Language Models (LLMs) influences their problem-solving and understanding, reinforcing the need to support productive LLM-uses that promote learning. This study evaluates the impact of a structured GPT platform designed to promote 'good' prompting behavior with data from 58 students in a graduate-level robotics course. The students were assigned to either an intervention group using the structured platform or a control group using ChatGPT…

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
Kemboi Cheruiyot, Nickson Kiprotich, Vyacheslav Kungurtsev, Kennedy Mugo, Vivian Mwirigi, Marvin Ngesa
https://arxiv.org/abs/2507.06278

A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
The increasing interest in research and innovation towards the development of autonomous agents presents a number of complex yet important scenarios of multiple AI Agents interacting with each other in an environment. The particular setting can be understood as exhibiting three possibly topologies of interaction - centrally coordinated cooperation, ad-hoc interaction and cooperation, and settings with noncooperative incentive structures. This article presents a comprehensive survey of all three…

VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Li Kang, Xiufeng Song, Heng Zhou, Yiran Qin, Jie Yang, Xiaohong Liu, Philip Torr, Lei Bai, Zhenfei Yin
https://arxiv.org/abs/2506.09049

VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Coordinating multiple embodied agents in dynamic environments remains a core challenge in artificial intelligence, requiring both perception-driven reasoning and scalable cooperation strategies. While recent works have leveraged large language models (LLMs) for multi-agent planning, a few have begun to explore vision-language models (VLMs) for visual reasoning. However, these VLM-based approaches remain limited in their support for diverse embodiment types. In this work, we introduce VIKI-Bench…