Small Models, Big Results: Training a Finance Agent for Under $500 — Charles Dickens, Snorkel AI

This post details the training of a 4 billion parameter model (Qwen3 4B) using reinforcement learning (RL) with the rLLM framework for under $500, which outperformed a 235 billion parameter model on financial question-answering tasks (FinQA dataset). The key insight is that disciplined tool use, rather than raw reasoning depth, was the bottleneck. The FinQA dataset was built from SEC 10-K filings with expert validation. The training utilized simple pass/fail rewards and demonstrated that specialized agents can be trained cost-effectively, with skills transferring to harder problems without degrading general tool use. This provides a blueprint for building specialized enterprise AI agents.