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BUILDER'S SANDBOX

Core Pattern

AI-generated implementation pattern based on this paper's core methodology.

Implementation pattern included in full analysis above.

MVP Investment

$9K - $13K
6-10 weeks
Engineering
$8,000
Cloud Hosting
$240
LLM API Credits
$500
SaaS Stack
$300
Domain & Legal
$100

6mo ROI

2-4x

3yr ROI

10-20x

Lightweight AI tools can reach profitability quickly. At $500/mo average contract, 20 customers = $10K MRR by 6mo, 200+ by 3yr.

Talent Scout

N

Narjes Nourzad

University of Southern California

C

Carlee Joe-Wong

Carnegie Mellon University

Find Similar Experts

RL experts on LinkedIn & GitHub

Founder's Pitch

"MIRA enhances reinforcement learning efficiency by integrating memory-structured LLM guidance, reducing reliance on continuous LLM queries while preserving policy convergence."

RL Integration with LLMsScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

2/4 signals

5

Series A Potential

1/4 signals

2.5

🔭 Research Neighborhood

Generating constellation...

~3-8 seconds

Why It Matters

The integration of LLMs into reinforcement learning addresses the sample complexity issue in environments with sparse or delayed rewards by providing structured guidance that accelerates learning.

Product Angle

This could be turned into a reinforcement learning development kit that integrates LLM guidance, offering enterprises a toolkit to optimize RL-based training on specific automation processes without extensive reliance on large external datasets.

Disruption

This approach could improve the efficiency of current RL-based systems which are often data and compute-intensive, reducing reliance on continuous real-time LLM aid.

Product Opportunity

The market is large for industries reliant on automation, like logistics and autonomous systems, which seek to improve decision-making and efficiency. Enterprises managing complex environments stand to benefit, thereby justifying investment in such tools.

Use Case Idea

Develop an AI tool for dynamic task planning in complex environments such as automated warehouses or autonomous vehicles, where real-time decision making is enhanced with structured memory from prior experiences and LLM insights.

Science

MIRA uses a memory graph co-constructed from agent experiences and LLM outputs to provide structured guidance in reinforcement learning. It reduces LLM queries by storing useful information in memory, which is then used to shape the agent's advantage estimations, thereby refining policy updates.

Method & Eval

The MIRA system was tested with benchmarks in RL environments known for sparse rewards. Empirical results showed MIRA's efficiency in reducing LLM queries while maintaining performance comparable to intensive LLM-dependent strategies.

Caveats

The strategy depends on the initial quality of LLM-derived guidance and may still be constrained by specific LLM capabilities. As tasks or environments grow more complex, the graph pruning might discard potentially useful scenarios.

Author Intelligence

Narjes Nourzad

LEAD
University of Southern California
nourzad@usc.edu

Carlee Joe-Wong

Carnegie Mellon University
cjoewong@andrew.cmu.edu

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2025Xu Wan, Wenyue Xu et al.
[2]
HalluLens: LLM Hallucination Benchmark
2025Yejin Bang, Ziwei Ji et al.
[3]
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
2024Frank F. Xu, Yufan Song et al.
[4]
Efficient Policy Adaptation with Contrastive Prompt Ensemble for Embodied Agents
2024Wonje Choi, Woo Kyung Kim et al.
[5]
DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models
2024Yongdong Wang, Runze Xiao et al.
[6]
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
2024Jianlan Luo, Charles Xu et al.
[7]
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2024Jiaxuan Gao, Shusheng Xu et al.
[8]
Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration
2024Yun Qu, Boyuan Wang et al.
[9]
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2024Sheila Schoepp, Masoud Jafaripour et al.
[10]
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2024Mark Towers, Ariel Kwiatkowski et al.
[11]
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2024Shreyas Basavatia, K. Murugesan et al.
[12]
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2024Siddhant Bhambri, Amrita Bhattacharjee et al.
[13]
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2024Murtaza Dalal, Tarun Chiruvolu et al.
[14]
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[15]
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2024Yuji Cao, Huan Zhao et al.
[16]
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
2024Yufei Wang, Zhanyi Sun et al.
[17]
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
2024S. Tonmoy, S. Zaman et al.
[18]
Integrating Planning and Deep Reinforcement Learning via Automatic Induction of Task Substructures
2024Jung-Chun Liu, Chi-Hsien Chang et al.
[19]
Memory-Augmented Deep Deterministic Policy Gradient
2024Qian Qiu, Fanyu Zeng et al.
[20]
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2023Rudra P. K. Poudel, Harit Pandya et al.

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