# CoTran: An LLM-based Code Translator using Reinforcement Learning with Feedback from Compiler and Symbolic Execution

arXiv: https://arxiv.org/abs/2306.06755 · HTML: https://mlsanity.com/paper/2306.06755/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 11 Jun 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://mlsanity.com/paper/2207.01780/) (2022)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [Execution-based Code Generation using Deep Reinforcement Learning](https://mlsanity.com/paper/2301.13816/) (2023)
- [Tree-to-tree Neural Networks for Program Translation](https://mlsanity.com/paper/1802.03691/) (2018)
- [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021)
- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [AVATAR: A Parallel Corpus for Java-Python Program Translation](https://mlsanity.com/paper/2108.11590/) (2021)
- [Pynguin: Automated Unit Test Generation for Python](https://mlsanity.com/paper/2202.05218/) (2022)
- [Compilable Neural Code Generation with Compiler Feedback](https://mlsanity.com/paper/2203.05132/) (2022)
- [Learning migration models for supporting incremental language migrations of software applications](https://mlsanity.com/paper/2103.09728/) (2021)

## Built on by (most cited first, 22 of 22)

- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [From Allies to Adversaries: Manipulating LLM Tool-Calling through Adversarial Injection](https://mlsanity.com/paper/2412.10198/) (2024)
- [Type-Constrained Code Generation with Language Models](https://mlsanity.com/paper/2504.09246/) (2025)
- [ProofBridge: Auto-Formalization of Natural Language Proofs in Lean via Joint Embeddings](https://mlsanity.com/paper/2510.15681/) (2025)
- [RLSF: Fine-tuning LLMs via Symbolic Feedback](https://mlsanity.com/paper/2405.16661/) (2024)
- [REFINE: Enhancing Program Repair Agents through Context-Aware Patch Refinement](https://mlsanity.com/paper/2510.03588/) (2025)
- [Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality](https://mlsanity.com/paper/2503.05860/) (2025)
- [Raw Pointer Rewriting with LLMs for Translating C to Safer Rust](https://mlsanity.com/paper/2505.04852/) (2025)
- [Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation](https://mlsanity.com/paper/2501.16050/) (2025)
- [Post-Incorporating Code Structural Knowledge into Pretrained Models via ICL for Code Translation](https://mlsanity.com/paper/2503.22776/) (2025)
- [CDRL: Certification-Driven Reinforcement Learning for Neutrino Flavor Model Discovery](https://mlsanity.com/paper/2608.20686/) (2026)
- [P4OMP: Retrieval-Augmented Prompting for OpenMP Parallelism in Serial Code](https://mlsanity.com/paper/2506.22703/) (2025)
- [CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora](https://mlsanity.com/paper/2604.18027/) (2026)
- [Toward Neurosymbolic Program Comprehension](https://mlsanity.com/paper/2502.01806/) (2025)
- [A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights](https://mlsanity.com/paper/2507.16037/) (2025)
- [The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers](https://mlsanity.com/paper/2601.02045/) (2026)
- [Revisiting the Role of Natural Language Code Comments in Code Translation](https://mlsanity.com/paper/2601.16661/) (2026)
- [Evaluating the Robustness of Proof Autoformalization in Lean 4](https://mlsanity.com/paper/2606.14867/) (2026)
- [Bridging Functional Correctness and Runtime Efficiency Gaps in LLM-Based Code Translation](https://mlsanity.com/paper/2606.17683/) (2026)
- [Bootstrapping Niche Multilingual Code Translation via Reinforcement Learning with Execution-Based Verifiable Supervision](https://mlsanity.com/paper/2608.13854/) (2026)
- [ORCA: Evaluating LLMs on Data Science Code Translation](https://mlsanity.com/paper/2609.30749/) (2026)
- [AIProver: Agentic Auto-Formalization of Mathematical Research via Certificate-Driven Evolving Harness](https://mlsanity.com/paper/2610.05367/) (2026)

## Abstract (arXiv)

In this paper, we present an LLM-based code translation method and an associated tool called CoTran, that translates whole-programs from one high-level programming language to another. Existing LLM-based code translation methods lack training to ensure that the translated code reliably compiles or bears substantial functional equivalence to the input code. In our work, we fine-tune an LLM using reinforcement learning, incorporating compiler feedback, and symbolic execution (symexec)-based testing feedback to assess functional equivalence between the input and output programs. The idea is to guide an LLM during fine-tuning, via compiler and symexec-based testing feedback, by letting it know how far it is from producing perfect translations. We conduct extensive experiments comparing CoTran with 14 other code translation tools, including human-written transpilers, LLM-based translation tools, and ChatGPT. Using a benchmark of over num57000 code pairs in Java and Python, we demonstrate that CoTran outperforms the other tools on relevant metrics such as compilation accuracy (CompAcc) and functional equivalence accuracy (FEqAcc). For example, in Python-to-Java translation, CoTran achieves 48.68% FEqAcc and 76.98% CompAcc, whereas the nearest competing tool (PLBART-base) gets 38.26% and 75.77% respectively. Additionally, CoTran, built on top of CodeT5, improves FEqAcc by +14.89% and CompAcc by +8.14% for Python-to-Java (resp., +12.94% and +4.30% for Java-to-Python).