# CHEHAB RL: Learning to Optimize Fully Homomorphic Encryption Computations

arXiv: https://arxiv.org/abs/2601.19367 · HTML: https://mlsanity.com/paper/2601.19367/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 75 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://mlsanity.com/paper/2103.06874/) (2021)
- [Ansor: Generating High-Performance Tensor Programs for Deep Learning](https://mlsanity.com/paper/2006.06762/) (2020)
- [Learning to Optimize Tensor Programs](https://mlsanity.com/paper/1805.08166/) (2018)
- [Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code](https://mlsanity.com/paper/1804.10694/) (2018)
- [NeuroVectorizer: End-to-End Vectorization with Deep Reinforcement Learning](https://mlsanity.com/paper/1909.13639/) (2019)
- [A Deep Learning Based Cost Model for Automatic Code Optimization](https://mlsanity.com/paper/2104.04955/) (2021)
- [AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.00671/) (2020)
- [EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation](https://mlsanity.com/paper/1912.11951/) (2019)
- [EffiCoder: Enhancing Code Generation in Large Language Models through Efficiency-Aware Fine-tuning](https://mlsanity.com/paper/2410.10209/) (2024)
- [Orion: A Fully Homomorphic Encryption Framework for Deep Learning](https://mlsanity.com/paper/2311.03470/) (2023)
- [SoK: Fully Homomorphic Encryption Compilers](https://mlsanity.com/paper/2101.07078/) (2021)
- [MLGOPerf: An ML Guided Inliner to Optimize Performance](https://mlsanity.com/paper/2207.08389/) (2022)
- [LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers](https://mlsanity.com/paper/2403.11522/) (2024)
- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [HEIR: A Universal Compiler for Homomorphic Encryption](https://mlsanity.com/paper/2508.11095/) (2025)
- [RL4ReAl: Reinforcement Learning for Register Allocation](https://mlsanity.com/paper/2204.02013/) (2022)
- [goSLP: Globally Optimized Superword Level Parallelism Framework](https://mlsanity.com/paper/1804.08733/) (2018)
- [Porcupine: A Synthesizing Compiler for Vectorized Homomorphic Encryption](https://mlsanity.com/paper/2101.07841/) (2021)

## Built on by (most cited first, 1 of 1)

- [Homomorphic Advantage Operator: Stabilizing Reinforcement Learning Under Fully Homomorphic Encryption Constraints](https://mlsanity.com/paper/2610.02074/) (2026)

## Abstract (arXiv)

Fully Homomorphic Encryption (FHE) enables computations directly on encrypted data, but its high computational cost remains a significant barrier. Writing efficient FHE code is a complex task requiring cryptographic expertise, and finding the optimal sequence of program transformations is often intractable. In this paper, we propose CHEHAB RL, a novel framework that leverages deep reinforcement learning (RL) to automate FHE code optimization. Instead of relying on predefined heuristics or combinatorial search, our method trains an RL agent to learn an effective policy for applying a sequence of rewriting rules to automatically vectorize scalar FHE code while reducing instruction latency and noise growth. The proposed approach supports the optimization of both structured and unstructured code. To train the agent, we synthesize a diverse dataset of computations using a large language model (LLM). We integrate our proposed approach into the CHEHAB FHE compiler and evaluate it on a suite of benchmarks, comparing its performance against Coyote, a state-of-the-art vectorizing FHE compiler. The results show that our approach generates code that is 5.3× faster in execution, accumulates 2.54× less noise, while the compilation process itself is 27.9× faster than Coyote (geometric means).