u/coslinedev

Hi everyone!

I am excited to share Financial-RLVR-10K — a fully open-source (MIT licensed) synthetic dataset of 10,000 execution-verified financial reasoning problems designed for RLVR / GRPO / PPO fine-tuning of open models (Qwen, Llama, DeepSeek, etc.).

Financial reasoning is infamous for math hallucinations. To ensure extreme data quality for verifiable reward training, every single problem in this dataset is 100% verified in a Python execution sandbox (reward = 1.0).

KEY HIGHLIGHTS & FEATURES:

100% Open & Free: Released under the MIT License.

10,000 Verified Records: Validated for syntax, logical flow, and exact numerical output via Python execution (exec).

19.5% Adversarial Edge Cases (1,950 samples): Teaches open models NOT to blindly compute impossible conditions (e.g., Discount Rate r <= g in Gordon Growth DCF, Option at Expiration T = 0 in Black-Scholes, or Zero Capital E + D = 0 in WACC).

Core Domains: DCF Valuation, Black-Scholes Option Pricing, Corporate WACC.

SAMPLE DATA SCHEMA:

{ "id": "fin-rlvr-10k-00042", "domain": "DCF Valuation", "is_edge_case": true, "prompt": "[EDGE CASE] Calculate DCF Terminal Value: FCF_1=$540, Discount Rate r=3.0%, Growth Rate g=5.0%.", "code_solution": "fcf, r, g = 540, 0.03, 0.05\nif r <= g:\n print("TRAP_DETECTED: Invalid Gordon Growth model condition (r <= g).")\nelse:\n print(f"RESULT: {fcf/(r-g):.4f}")", "ground_truth": "TRAP_DETECTED", "total_reward": 1.0, "status": "VERIFIED" }

LINKS & RESOURCES: Hugging Face Dataset: https://huggingface.co/datasets/coslinedev/financial-rlvr-10k-enterprise

Hope this dataset helps the open-source AI community train stronger, more robust financial reasoning models. Feel free to use, audit, or build upon it! Feedback and contributions are always welcome.

Hi everyone,

One major pain point when using general LLMs (like GPT-4 or standard Llama) for financial math and quantitative coding is code execution failure or subtle logic/math hallucinations in edge cases.

To address this, I built FinCode-Reasoning-3B, an open-source model fine-tuned on FinCode-Reasoning-v1 (a dataset where 100% of the Python scripts are verified via sandboxed execution & unit tests).

Key Highlights:

• 100% Verified Execution: Every training example was compiled and unit-tested in a sandboxed Python environment to guarantee syntactically and mathematically correct code output.
• Lightweight & Private: Built on Qwen2.5-3B-Instruct, meaning it runs blazingly fast locally on standard consumer GPUs/macBooks without leaking sensitive financial data to cloud APIs.
• Financial Scope: Handles Black-Scholes option pricing, Declining Balance Depreciation (DDB), Interest Tax Shields, WACC, and custom corporate finance calculations.
• Permissive License: Released under Apache 2.0 (free for personal and commercial research).

Example Code Output (Black-Scholes Call/Put):

Python

import math
from scipy.stats import norm

def black_scholes(S, K, T, r, sigma, option_type="call"):
    d1 = (math.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * math.sqrt(T))
    d2 = d1 - sigma * math.sqrt(T)
    if option_type.lower() == "call":
        return S * norm.cdf(d1) - K * math.exp(-r * T) * norm.cdf(d2)
    elif option_type.lower() == "put":
        return K * math.exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)

Both the fine-tuned weights and the 100% execution-verified dataset are available on Hugging Face:

• Model: [https://huggingface.co/coslinedev/Qwen2.5-3B-FinCode-Reasoning-Full]
• Dataset: [https://huggingface.co/datasets/coslinedev/FinCode-Reasoning-v1]

Would love to hear your feedback or suggestions on additional financial/quant math domains to add to v2!