# Curriculum-Guided Reinforcement Learning for Synthesizing Gas-Efficient Financial Derivatives Contracts

arXiv: https://arxiv.org/abs/2509.23976 · HTML: https://mlsanity.com/paper/2509.23976/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Slither: A Static Analysis Framework For Smart Contracts](https://mlsanity.com/paper/1908.09878/) (2019)
- [GASOL: Gas Analysis and Optimization for Ethereum Smart Contracts](https://mlsanity.com/paper/1912.11929/) (2019)
- [Guiding LLM-based Smart Contract Generation with Finite State Machine](https://mlsanity.com/paper/2505.08542/) (2025)
- [A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler](https://mlsanity.com/paper/2409.11068/) (2024)
- [Semantic Interoperability on Blockchain by Generating Smart Contracts Based on Knowledge Graphs](https://mlsanity.com/paper/2409.12171/) (2024)
- [AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts](https://mlsanity.com/paper/2506.01063/) (2025)

## Abstract (arXiv)

Smart contract-based automation of financial derivatives offers substantial efficiency gains, but its real-world adoption is constrained by the complexity of translating financial specifications into gas-efficient executable code. In particular, generating code that is both functionally correct and economically viable from high-level specifications, such as the Common Domain Model (CDM), remains a significant challenge. This paper introduces a Reinforcement Learning (RL) framework to generate functional and gas-optimized Solidity smart contracts directly from CDM specifications. We employ a Proximal Policy Optimization (PPO) agent that learns to select optimal code snippets from a pre-defined library. To manage the complex search space, a two-phase curriculum first trains the agent for functional correctness before shifting its focus to gas optimization. Our empirical results show the RL agent learns to generate contracts with significant gas savings, achieving cost reductions of up to 35.59% on unseen test data compared to unoptimized baselines. This work presents a viable methodology for the automated synthesis of reliable and economically sustainable smart contracts, bridging the gap between high-level financial agreements and efficient on-chain execution.