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Ziyi He
The University of Hong Kong
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Yinghao Zhu
The University of Hong Kong
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Sitong Wu
The Chinese University of Hong Kong
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Founder's Pitch
"SearchGym offers cost-effective, high-fidelity simulation for training RL-based search agents without expensive API calls."
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Why It Matters
This research tackles the high costs associated with training RL-based search agents by eliminating the need for live Web API interaction through high-fidelity simulation, offering a scalable solution for robust model development.
Product Angle
Package SearchGym as a SaaS solution for companies in need of training AI systems with robust search capabilities, avoiding high API fees, and benefiting from scalable, efficient learning processes.
Disruption
SearchGym could replace costly web API interactions currently used in training RL search agents, offering a more cost-effective and scalable alternative.
Product Opportunity
The need to develop autonomous search agents in AI-heavy industries is growing, especially where real-time data interaction is cost-prohibitive. Companies with large-scale AI operations and data-driven decision-making processes would likely invest in simulation solutions like SearchGym.
Use Case Idea
Develop a SaaS platform providing high-fidelity simulation environments similar to SearchGym for enterprises training custom AI search agents.
Science
SearchGym constructs a verifiable knowledge graph and aligned document corpus within a simulated environment. It uses a curriculum learning methodology in RL settings, allowing agents to progressively learn complex reasoning tasks with purified feedback.
Method & Eval
The research uses a simulation experiment with synthetic data to test RL agents, demonstrating that models trained with SearchGym outperform baselines, showing a 10% improvement over web-enhanced standards across multiple benchmarks.
Caveats
The key limitations may include specific applicability only to environments where control over data generation is possible. The simulation accuracy might not fully reflect all real-world complexities.
Author Intelligence
Xichen Zhang
The Hong Kong University of Science and Technology
xichenzhang879@gmail.com
Ziyi He
The University of Hong Kong
Yinghao Zhu
The University of Hong Kong
Sitong Wu
The Chinese University of Hong Kong
Shaozuo Yu
The Chinese University of Hong Kong
Meng Chu
The Hong Kong University of Science and Technology
Wenhu Zhang
The Hong Kong University of Science and Technology
Haoru Tan
The University of Hong Kong
Jiaya Jia
The Hong Kong University of Science and Technology