Show HN: We trained a 32B model to beat Opus 4 at credit card optimization

Category: ai-ml

Tags: llm-finetuning, reinforcement-learning, financial-ai

Score: 5.3/10 (Innovation: 7, Technical: 6, Documentation: 4, Utility: 3)

This project demonstrates applying Group Relative Policy Optimization (GRPO) to train a 32B parameter model for personalized credit card recommendations, showing it can outperform Claude 3.5 Sonnet on this specific financial optimization task. It's interesting because it applies a relatively new RLHF-alternative training method to a practical, non-academic domain with structured financial data.

Target audience: data-engineers, ml-researchers, fintech-developers

Repository: https://huggingface.co/spaces/endishai/blog-grpo-credit-cards

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