Tootfinder

Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer
Sooraj Sathish, Keshav Goyal, Raghuram Bharadwaj Diddigi
https://arxiv.org/abs/2509.24947 https:/…

Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer
Deep Reinforcement Learning (RL) has demonstrated success in solving complex sequential decision-making problems by integrating neural networks with the RL framework. However, training deep RL models poses several challenges, such as the need for extensive hyperparameter tuning and high computational costs. Transfer learning has emerged as a promising strategy to address these challenges by enabling the reuse of knowledge from previously learned tasks for new, related tasks. This avoids the nee…

Grouped Satisficing Paths in Pure Strategy Games: a Topological Perspective
Yanqing Fu, Chao Huang, Chenrun Wang, Zhuping Wang
https://arxiv.org/abs/2509.23157 https://

Grouped Satisficing Paths in Pure Strategy Games: a Topological Perspective
In game theory and multi-agent reinforcement learning (MARL), each agent selects a strategy, interacts with the environment and other agents, and subsequently updates its strategy based on the received payoff. This process generates a sequence of joint strategies $(s^t)_{t \geq 0}$, where $s^t$ represents the strategy profile of all agents at time step $t$. A widely adopted principle in MARL algorithms is "win-stay, lose-shift", which dictates that an agent retains its current strategy if it ac…

ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding
Sining Zhoubian, Dan Zhang, Yuxiao Dong, Jie Tang
https://arxiv.org/abs/2508.19576 h…

ReST-RL: Achieving Accurate Code Reasoning of LLMs with Optimized Self-Training and Decoding
With respect to improving the reasoning accuracy of LLMs, the representative reinforcement learning (RL) method GRPO faces failure due to insignificant reward variance, while verification methods based on process reward models (PRMs) suffer from difficulties with training data acquisition and verification effectiveness. To tackle these problems, this paper introduces ReST-RL, a unified LLM RL paradigm that significantly improves LLM's code reasoning ability by combining an improved GRPO algorit…

Towards Production-Worthy Simulation for Autonomous Cyber Operations
Konur Tholl, Mariam El Mezouar, Ranwa Al Mallah
https://arxiv.org/abs/2508.19278 https://

Towards Production-Worthy Simulation for Autonomous Cyber Operations
Simulated environments have proven invaluable in Autonomous Cyber Operations (ACO) where Reinforcement Learning (RL) agents can be trained without the computational overhead of emulation. These environments must accurately represent cybersecurity scenarios while producing the necessary signals to support RL training. In this study, we present a framework where we first extend CybORG's Cage Challenge 2 environment by implementing three new actions: Patch, Isolate, and Unisolate, to better repres…

Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
Austin Braniff, Yuhe Tian
https://arxiv.org/abs/2508.16474 https://arxiv.org…

Reinforcement Learning-based Control via Y-wise Affine Neural Networks (YANNs)
This work presents a novel reinforcement learning (RL) algorithm based on Y-wise Affine Neural Networks (YANNs). YANNs provide an interpretable neural network which can exactly represent known piecewise affine functions of arbitrary input and output dimensions defined on any amount of polytopic subdomains. One representative application of YANNs is to reformulate explicit solutions of multi-parametric linear model predictive control. Built on this, we propose the use of YANNs to initialize RL a…

A closer look at Peru’s Amazon reveals new mining trends, deforestation https://news.mongabay.com/2025/10/a-closer-look-at-perus-amazon-reveals-new-mining-trends-deforestation/

A closer look at Peru’s Amazon reveals new mining trends, deforestation
For years, gold mining has been one of Peru’s most pressing environmental challenges, with illegal operations clearing the Amazon Rainforest and polluting rivers with chemicals. The industry continues to grow, as miners spread into new parts of the country and adopt different ways of working. One recent project that maps mining operations and their expansion […]

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Chaojun Nie, Jun Zhou, Guanxiang Wang, Shisong Wud, Zichen Wang
https://arxiv.org/abs/2509.20162

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets. Knowledge scarcity and temporal lag create knowledge gaps for domain applications. While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations. Continual Pre-Training (CPT) treats all tokens in domain documents with…

Tree Search for LLM Agent Reinforcement Learning
Yuxiang Ji, Ziyu Ma, Yong Wang, Guanhua Chen, Xiangxiang Chu, Liaoni Wu
https://arxiv.org/abs/2509.21240 https://

Tree Search for LLM Agent Reinforcement Learning
Recent advances in reinforcement learning (RL) have significantly enhanced the agentic capabilities of large language models (LLMs). In long-term and multi-turn agent tasks, existing approaches driven solely by outcome rewards often suffer from the problem of sparse supervision. To address the challenge, we propose Tree-based Group Relative Policy Optimization (Tree-GRPO), a grouped agent RL method based on tree search, where each tree node represents the complete agent interaction step. By sha…

Attackers Strike Back? Not Anymore - An Ensemble of RL Defenders Awakens for APT Detection
Sidahmed Benabderrahmane, Talal Rahwan
https://arxiv.org/abs/2508.19072 https://

Attackers Strike Back? Not Anymore - An Ensemble of RL Defenders Awakens for APT Detection
Advanced Persistent Threats (APTs) represent a growing menace to modern digital infrastructure. Unlike traditional cyberattacks, APTs are stealthy, adaptive, and long-lasting, often bypassing signature-based detection systems. This paper introduces a novel framework for APT detection that unites deep learning, reinforcement learning (RL), and active learning into a cohesive, adaptive defense system. Our system combines auto-encoders for latent behavioral encoding with a multi-agent ensemble of …

RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs
Hangzhan Jin, Sicheng Lv, Sifan Wu, Mohammad Hamdaqa
https://arxiv.org/abs/2508.16546

RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs
Training large language models (LLMs) from scratch is increasingly impractical, making post-training methods such as supervised fine-tuning (SFT) and reinforcement-learning fine-tuning (RL-FT, e.g., PPO) central to modern practice. Using an out-of-distribution (OOD) variant of the 24-point card game and new spectrum-based diagnostics, we revisit how these two stages reshape model representation and OOD performance. Our key findings are- (1) RL-FT can restore much of the OOD performance loss fro…