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).

Fully Homomorphic Encryption (FHE) enables computations to be performed directly on encrypted data without requiring decryption, providing strong privacy guarantees. However, FHE remains computationally expensive, and writing efficient FHE programs is a complex, error-prone, and time-consuming task that demands significant cryptographic expertise. Programmers are often unaware of available optimizations, and applying them manually requires substantial effort. In this paper, we present CHEHAB, a compiler that automatically vectorizes scalar code, optimizes it, and generates highly efficient FHE programs. CHEHAB supports both structured and unstructured code and takes as input programs written in a domain-specific language embedded in C++. It relies on a Term Rewriting System (TRS) based on equality saturation to simplify and transform programs. CHEHAB targets two key challenges in FHE compilation: (1) automatic vectorization of scalar code, and (2) reduction of instruction execution latency and ciphertext noise growth. By leveraging equality saturation, CHEHAB explores a large optimization space to reduce instruction count and circuit depth while improving vector utilization. Experimental evaluation on a set of representative kernels shows that CHEHAB outperforms Coyote, a state-of-the-art vectorizing compiler for FHE. On average, CHEHAB generates code that is 7.38× faster at runtime, incurs 2.49× less accumulated noise, and achieves 251× faster compilation time. CHEHAB is released as an open-source compiler to support reproducibility and further research in FHE compilation.