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Yirou Ge
PayPal AI
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Yixi Li
PayPal AI
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Alec Chiu
PayPal AI
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Shivani Shekhar
PayPal AI
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Founder's Pitch
"Efficiently optimize reasoning in AI models for latency-sensitive applications via Ada-RS."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
Quick Build
4/4 signals
Series A Potential
2/4 signals
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Why It Matters
This research is important as it addresses the critical issue of efficiency in AI models, which often face cost and latency constraints in practical applications like customer service or e-commerce platforms. Ada-RS enhances reasoning efficiency without sacrificing accuracy, allowing AI applications to be more responsive and cost-effective.
Product Angle
To productize, create an API or SDK that can integrate Ada-RS into existing AI models used by companies focusing on latency-sensitive interactions. Market it towards sectors that require quick customer interactions, like e-commerce or banking.
Disruption
Ada-RS replaces existing, less efficient AI reasoning processes, offering a significant improvement in speed and cost-effectiveness for similar accuracy.
Product Opportunity
This solution targets a substantial market, as many businesses in fields like e-commerce, financial services, and IT support are moving towards AI solutions for customer interaction but face challenges with the cost and speed of responses.
Use Case Idea
Commercial application could be in developing AI customer service chatbots that efficiently manage reasoning processes, balancing response quality and speed to handle user queries more effectively at a lower computational cost.
Science
The paper introduces Adaptive Rejection Sampling (Ada-RS), an approach to filter learning samples for AI models, particularly in tool-using contexts. It scores various outputs with an adaptive length-penalized reward to maintain efficiency. By retaining only high-reward outputs, the method maintains accuracy while reducing token use.
Method & Eval
Tested using the Qwen3-8B model in an e-commerce setting. Results showed substantial reductions in token usage and thinking rate while maintaining, or improving, tool call accuracy, demonstrating enhanced efficiency in model reasoning.
Caveats
The main limitation lies in the potential variability of Ada-RS's effectiveness across different domains, especially if the nature of tasks is drastically different from those tested. The reliance on synthesized datasets for evaluation might not represent all real-world scenarios.
Author Intelligence
Yirou Ge
PayPal AI
Yixi Li
PayPal AI
Alec Chiu
PayPal AI
Shivani Shekhar
PayPal AI
Zijie Pan
PayPal AI
Avinash Thangali
PayPal AI
Yun-Shiuan Chuang
PayPal AI
Chaitanya Kulkarni
PayPal AI
Uma Kona
PayPal AI
Linsey Pang
PayPal AI
Prakhar Mehrotra
PayPal AI