Imagine if AI could think like a seasoned finance analyst, providing concise, balanced rationale and verifiable outputs. Sounds like a dream, right? Well, I’m excited to share a breakthrough model that’s making this a reality.
I trained a tiny AI model to think like a finance analyst by enforcing a strict output contract and rewarding it only when the output is verifiably correct. This means the model provides structured outputs, including concise reasoning, sentiment, and confidence levels.
What Makes This Model Special
This model is special because it’s small, fast, and auditable. It runs on modest hardware with low latency and cost, making it perfect for real-world applications. Plus, its structured outputs are easy to log, QA, and govern.
How It Works
The model uses a combination of techniques, including reinforcement learning with verifiable rewards, format gate, reasoning heuristics, FinBERT alignment, and confidence calibration. This allows it to provide accurate and reliable outputs, even in complex financial scenarios.
Early Results
The early results are promising, with the model showing cleaner structure, better agreement on mixed headlines, and steadier confidence compared to the base model.
What’s Next
I’m planning to make further improvements, including adding a more robust reward evaluation and better synthetic data. My goal is to make small models like this one incredibly intelligent in specific domains.
Get Involved
If you’re interested in exploring the code and contributing to the project, check out the GitHub repository: Reinforcement-learning-with-verifable-rewards-Learnings/projects/financial-reasoning-enhanced at main · Pavankunchala/Reinforcement-learning-with-verifable-rewards-Learnings
And if you’re looking for a talented AI engineer, I’m currently exploring new opportunities in the LLM / Computer Vision space.
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*Check out my portfolio: Pavan Kunchala – AI Engineer & Full-Stack Developer*