rLLM/rLLM-FinQA-4B

FinQA

Training Financial Agents with Reinforcement Learning

FinQA Overview

FinQA is a financial question-answering agent fine-tuned from Qwen3-4B-Instruct-2507 using reinforcement learning (RL). The model answers questions about SEC 10-K financial statements using specialized tools (SQL queries, table lookup, calculators), achieving 59.70% accuracy on Snorkel Finance Benchmark and 26.6% on Snorkel Finance Reasoning.

Data

Our training dataset is built from SEC 10-K filings and consists of 5,110 question-answer pairs across:

• 207 companies spanning multiple sectors
• 6,923 financial tables extracted from 10-K filings
• Single-table questions: Direct lookups and calculations from individual tables
• Multi-table questions: Cross-table reasoning requiring data from multiple sources

The dataset is available on HuggingFace.

Tools

The agent uses 4 specialized tools for financial analysis:

Tool	Description
get_table_names	List available tables for a given company
get_table_info	Get table metadata, columns, dtypes, and sample values
sql_query	Execute SQL queries on financial tables (SQLite)
calculator	Evaluate mathematical expressions

Training

We fine-tune Qwen3-4B-Instruct-2507 using GRPO with LLM-as-judge rewards for correctness evaluation. A more detailed description of the training recipe can be found in our documentation.

Evaluation

Model	FinQA	FinQA Reasoning
Qwen3-4B-Instruct-2507 (Base)	27.90%	13.90%
gpt-5-nano-2025-08-07	50.00%	26.60%
Qwen3-235B-A22B	51.37%	18.90%
rLLM-FinQA-4B (Ours)	59.70%	26.60%
Gemini-2.5-Pro-Preview	60.60%	34.60%
GPT-4.1-2025-04-14	62.70%	37.90%
o3-mini-2025-01-31	63.79%	30.37%

Serving FinQA

Start a vLLM server and run the agent:

python -m vllm.entrypoints.openai.api_server \
    --model rLLM/rLLM-FinQA-4B \
    --host 0.0.0.0 \
    --port 30000 \
    --dtype bfloat16

python -m projects.finqa.run_finqa

For detailed setup instructions, see the project README.

Acknowledgement

• This is a joint collaboration between the rLLM team at UC Berkeley and Snorkel AI.
• Our model is trained on top of Qwen3-4B-Instruct-2507.
• Our work is done as part of Berkeley Sky Computing Lab.

Citation

@misc{rllm2026finqa,
  title={FinQA: Training Financial Agents with Reinforcement Learning},
  author={Manan Roongta and Sijun Tan and Bhavishya Pohani and Charles Dickens and Christopher Glaze},
  year={2026},
  howpublished={\url{https://rllm-project.com/post.html?post=finqa.md}},
  note={Blog Post}
}