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

תקציר מקורי באנגליתA 4 billion parameter model, trained with RL for under $500, beat its 235 billion parameter sibling on real financial questions. Charles Dickens, research scientist at Snorkel AI, shares how Snorkel and UC Berkeley's Sky Computing Lab trained a small model to beat a much bigger one. Working from SEC 10-K filings, they built FinQA, a financial question-answering dataset with expert-validated answers, and found even frontier models hallucinating table schemas, flooding their own context, and repeating failed strategies. Using the open-source rLLM framework and reinforcement learning with a simple pass/fail reward, they trained a Qwen3 4B model for under $500 that reached about 60% versus 51% for the 235B model. The skills transferred to harder multi-table questions without hurting general to