mlsanityData to 14 Oct 2026

Trust Your Memory: Verifiable Control of Smart Homes through Reinforcement Learning with Multi-dimensional Rewards

arXiv 2604.10110 ↗First posted 11 Apr 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL training of language and agent models

Abstract (arXiv)

Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how smart home assistants understand user queries to control devices in real time, their ability to perform memory-driven device control remains challenging from both evaluation and methodological perspectives. In terms of evaluation, existing benchmarks either focus on immediate device control or general open-domain memory retrieval tasks, and therefore cannot effectively evaluate a model's ability to perform memory-driven device control. Methodologically, while memory-driven device control can be approached using Reinforcement Learning, conventional RL methods generally rely on outcome-based supervision (i.e., whether the final task is achieved). This lack of intermediate feedback can lead to sub-optimal performance or local failures in fine-grained memory management tasks (adding, updating, deleting, and utilizing). To address these issues, we first release MemHomeLife, built from anonymized real-world long-term user interaction logs. To enable more fine-grained evaluation of different memory-related subtasks, we further construct MemHome, the first benchmark designed to systematically evaluate memory-driven device control in smart home scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL training of language and agent models, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 21 references that are in the map. First posted on arXiv 11 Apr 2026.

Builds on

9 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025VerIF: Verification Engineering for Reinforcement Learning in Instruction Following11 Jun

Other references · 8

  1. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 830
  2. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
  3. 2023RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedbackcited by 625
  4. 2025DeepSeekMath-V2: Towards Self-Verifiable Mathematical Reasoningcited by 94
  5. 2025RLPR: Extrapolating RLVR to General Domains without Verifierscited by 85
  6. 2025MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systemscited by 61
  7. 2025Beyond Verifiable Rewards: Scaling Reinforcement Learning for Language Models to Unverifiable Datacited by 29
  8. 2025HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devicescited by 15

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  1. 2026SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
  2. 2026MiCU: End-to-End Smart Home Command Understanding with Large Language Model
  3. 2023Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models
  4. 2024Predicting User Behavior in Smart Spaces with LLM-Enhanced Logs and Personalized Prompts
  5. 2026PersonalHomeBench: Evaluating Agents in Personalized Smart Homes
  6. 2026AMemGym: Interactive Memory Benchmarking for Assistants in Long-Horizon Conversations
  7. 2026Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions
  8. 2026MobileMem: Learning from a Year of Mobile Experiences