NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agent
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915712414515200 |
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| author | Garg, Sudhanshu Wang, Andrew Kulkarni, Chaitanya Sahami, Ali Farahani, Farhad Chuang, Sean Yun-Shiuan Wan, Jian Manoharan, Srinivasan Kona, Uma Sharma, Nitin Pang, Linsey Mehrotra, Prakhar Clark, Jessica Moyou, Mark |
| author_facet | Garg, Sudhanshu Wang, Andrew Kulkarni, Chaitanya Sahami, Ali Farahani, Farhad Chuang, Sean Yun-Shiuan Wan, Jian Manoharan, Srinivasan Kona, Uma Sharma, Nitin Pang, Linsey Mehrotra, Prakhar Clark, Jessica Moyou, Mark |
| contents | We present the development and optimization of PayPal's Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal platform. Through our strategic partnership with NVIDIA, we leveraged the NeMo Framework for LLM model fine-tuning to enhance agent performance. Specifically, we optimized the Search and Discovery agent by replacing our base model with a fine-tuned Nemotron small language model (SLM).
We conducted comprehensive experiments using the llama3.1-nemotron-nano-8B-v1 architecture, training LoRA-based models through systematic hyperparameter sweeps across learning rates, optimizers (Adam, AdamW), cosine annealing schedules, and LoRA ranks. Our contributions include: (1) the first application of NVIDIA's NeMo Framework to commerce-specific agent optimization, (2) LLM powered fine-tuning strategy for retrieval-focused commerce tasks, (3) demonstration of significant improvements in latency and cost while maintaining agent quality, and (4) a scalable framework for multi-agent system optimization in production e-commerce environments. Our results demonstrate that the fine-tuned Nemotron SLM effectively resolves the key performance issue in the retrieval component, which represents over 50\% of total agent response time, while maintaining or enhancing overall system performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agent Garg, Sudhanshu Wang, Andrew Kulkarni, Chaitanya Sahami, Ali Farahani, Farhad Chuang, Sean Yun-Shiuan Wan, Jian Manoharan, Srinivasan Kona, Uma Sharma, Nitin Pang, Linsey Mehrotra, Prakhar Clark, Jessica Moyou, Mark Artificial Intelligence We present the development and optimization of PayPal's Commerce Agent, powered by NEMO-4-PAYPAL, a multi-agent system designed to revolutionize agentic commerce on the PayPal platform. Through our strategic partnership with NVIDIA, we leveraged the NeMo Framework for LLM model fine-tuning to enhance agent performance. Specifically, we optimized the Search and Discovery agent by replacing our base model with a fine-tuned Nemotron small language model (SLM). We conducted comprehensive experiments using the llama3.1-nemotron-nano-8B-v1 architecture, training LoRA-based models through systematic hyperparameter sweeps across learning rates, optimizers (Adam, AdamW), cosine annealing schedules, and LoRA ranks. Our contributions include: (1) the first application of NVIDIA's NeMo Framework to commerce-specific agent optimization, (2) LLM powered fine-tuning strategy for retrieval-focused commerce tasks, (3) demonstration of significant improvements in latency and cost while maintaining agent quality, and (4) a scalable framework for multi-agent system optimization in production e-commerce environments. Our results demonstrate that the fine-tuned Nemotron SLM effectively resolves the key performance issue in the retrieval component, which represents over 50\% of total agent response time, while maintaining or enhancing overall system performance. |
| title | NEMO-4-PAYPAL: Leveraging NVIDIA's Nemo Framework for empowering PayPal's Commerce Agent |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2512.21578 |