Towards Reliable Financial Agents: How a 4B Model Outsmarted a 235B Giant

Official Schedule Context

Large generalist models have excellent reasoning but this does not necessarily imply specialized

knowledge and tool calling capabilities. They can still hallucinate column names, ignore

constraints, and generate SQL that returns nonsensical results. The problem isn't intelligence it's

reliability and specialization. In this talk we'll show how a 4B model was fine-tuned to outperform

a 235B model on real financial analysis tasks. The key was not adding more reasoning ability, but

enforcing tool discipline. Using synthetic data generation and reinforcement learning with the open-

source rLLM framework, the model learned to explore schemas, validate outputs, and retry failures

instead of hallucinating confident nonsense. One key result: tool-use fundamentals generalize.

Training on simple tool interactions transferred to much harder, multi-step financial tasks. If

you're building LLM systems that interact with databases, APIs, or internal tools, this talk focuses

on the behaviors that actually matter and how to teach them without frontier-scale compute.

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