:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Gupta, Ravi, Haider, Shabista
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2512.20623
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment
by: Sajid, Md. Ashiq Ul Islam, et al.
Published: (2026)

SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents
by: Seo, Gyuhyeon, et al.
Published: (2025)

SkyRL-Agent: Efficient RL Training for Multi-turn LLM Agent
by: Cao, Shiyi, et al.
Published: (2025)

Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning
by: Gao, Guanyu, et al.
Published: (2019)

Deep Reinforcement Learning for Optimizing Energy Consumption in Smart Grid Systems
by: Alsheikhi, Abeer, et al.
Published: (2026)

Applying Reinforcement Learning to Optimize Traffic Light Cycles
by: Son, Seungah, et al.
Published: (2024)

SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
by: Li, Kuan, et al.
Published: (2026)

FlipLLM: Efficient Bit-Flip Attacks on Multimodal LLMs using Reinforcement Learning
by: Khalil, Khurram, et al.
Published: (2025)

DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home
by: Safaei, Danial, et al.
Published: (2023)

BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
by: Zhao, Jiayu, et al.
Published: (2026)

PersonalHomeBench: Evaluating Agents in Personalized Smart Homes
by: Bharadwaj, Manasa, et al.
Published: (2026)

HomeFlow: A Data Flywheel for Smart Home Agent Training with Verifiable Simulation
by: Gu, Yi, et al.
Published: (2026)

Counteractive RL: Rethinking Core Principles for Efficient and Scalable Deep Reinforcement Learning
by: Korkmaz, Ezgi
Published: (2026)

Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems
by: Zhu, Ming, et al.
Published: (2023)

Energy-Efficient Deep Reinforcement Learning with Spiking Transformers
by: Uddin, Mohammad Irfan, et al.
Published: (2025)

RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning
by: Hao, Qianyue, et al.
Published: (2025)

LightRouter: Towards Efficient LLM Collaboration with Minimal Overhead
by: Zhang, Yifan, et al.
Published: (2025)

Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards
by: Guo, Kai-Yuan, et al.
Published: (2026)

A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems
by: Salem, Mohamed Abdallah, et al.
Published: (2025)

SpikeRL: A Scalable and Energy-efficient Framework for Deep Spiking Reinforcement Learning
by: Tahmid, Tokey, et al.
Published: (2025)

LightSearcher: Efficient DeepSearch via Experiential Memory
by: Lan, Hengzhi, et al.
Published: (2025)

ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents
by: Zhang, Hao, et al.
Published: (2026)

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?
by: Chen, Wanyi, et al.
Published: (2026)

Continual Quantization-Aware Pre-Training: When to transition from 16-bit to 1.58-bit pre-training for BitNet language models?
by: Nielsen, Jacob, et al.
Published: (2025)

EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge
by: Mounesan, Motahare, et al.
Published: (2024)

Targeted Bit-Flip Attacks on LLM-Based Agents
by: Wang, Jialai, et al.
Published: (2026)

LlamaRL: A Distributed Asynchronous Reinforcement Learning Framework for Efficient Large-scale LLM Training
by: Wu, Bo, et al.
Published: (2025)

Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization
by: Qiu, Dong, et al.
Published: (2025)

Representation Learning For Efficient Deep Multi-Agent Reinforcement Learning
by: Huh, Dom, et al.
Published: (2024)

Illuminating the Three Dogmas of Reinforcement Learning under Evolutionary Light
by: Hamidi, Mani, et al.
Published: (2025)

IntentRL: Training Proactive User-intent Agents for Open-ended Deep Research via Reinforcement Learning
by: Luo, Haohao, et al.
Published: (2026)

AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
by: Xi, Zhiheng, et al.
Published: (2025)

Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning
by: Xie, Tian, et al.
Published: (2025)

MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents
by: Xu, Yifan, et al.
Published: (2025)

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
by: Lee, Deokjae, et al.
Published: (2025)

OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
by: Bokade, Rohit, et al.
Published: (2024)

Light-weight probing of unsupervised representations for Reinforcement Learning
by: Zhang, Wancong, et al.
Published: (2022)

ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents
by: Lai, Hanyu, et al.
Published: (2025)

DRAFT-RL: Multi-Agent Chain-of-Draft Reasoning for Reinforcement Learning-Enhanced LLMs
by: Li, Yuanhao, et al.
Published: (2025)

Token-Efficient RL for LLM Reasoning
by: Lee, Alan, et al.
Published: (2025)