# RM -RF: Reward Model for Run-Free Unit Test Evaluation

arXiv: https://arxiv.org/abs/2601.13097 · HTML: https://mlsanity.com/paper/2601.13097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 30 of 31)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018)
- [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024)
- [Unit Test Case Generation with Transformers and Focal Context](https://mlsanity.com/paper/2009.05617/) (2020)
- [RLTF: Reinforcement Learning from Unit Test Feedback](https://mlsanity.com/paper/2307.04349/) (2023)
- [Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction](https://mlsanity.com/paper/2209.11515/) (2022)
- [Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning](https://mlsanity.com/paper/2506.03136/) (2025)
- [TestGenEval: A Real World Unit Test Generation and Test Completion Benchmark](https://mlsanity.com/paper/2410.00752/) (2024)
- [Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing](https://mlsanity.com/paper/2308.16557/) (2023)
- [Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback](https://mlsanity.com/paper/2403.16792/) (2024)
- [Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study](https://mlsanity.com/paper/2403.08604/) (2024)
- [Teaching Language Models to Critique via Reinforcement Learning](https://mlsanity.com/paper/2502.03492/) (2025)
- [TESTEVAL: Benchmarking Large Language Models for Test Case Generation](https://mlsanity.com/paper/2406.04531/) (2024)
- [Multi-Turn Code Generation Through Single-Step Rewards](https://mlsanity.com/paper/2502.20380/) (2025)
- [Process Supervision-Guided Policy Optimization for Code Generation](https://mlsanity.com/paper/2410.17621/) (2024)
- [Mutation-Guided LLM-based Test Generation at Meta](https://mlsanity.com/paper/2501.12862/) (2025)
- [Process-Supervised Reinforcement Learning for Code Generation](https://mlsanity.com/paper/2502.01715/) (2025)
- [Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation](https://mlsanity.com/paper/2412.15118/) (2024)
- [Learning to Generate Unit Tests for Automated Debugging](https://mlsanity.com/paper/2502.01619/) (2025)
- [TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models](https://mlsanity.com/paper/2409.17561/) (2024)
- [Learning to Generate Unit Test via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2508.21107/) (2025)
- [Regression Language Models for Code](https://mlsanity.com/paper/2509.26476/) (2025)
- [RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation](https://mlsanity.com/paper/2502.09183/) (2025)

## Built on by (most cited first, 2 of 2)

- [Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring](https://mlsanity.com/paper/2605.00754/) (2026)
- [TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance](https://mlsanity.com/paper/2601.18241/) (2026)

## Abstract (arXiv)

We present RM-RF, a lightweight reward model for run-free evaluation of automatically generated unit tests. Instead of repeatedly compiling and executing candidate tests, RM-RF predicts - from source and test code alone - three execution-derived signals: (1) whether the augmented test suite compiles and runs successfully, (2) whether the generated test cases increase code coverage, and (3) whether the generated test cases improve the mutation kill rate. To train and evaluate RM-RF we assemble a multilingual dataset (Java, Python, Go) of focal files, test files, and candidate test additions labeled by an execution-based pipeline, and we release an associated dataset and methodology for comparative evaluation. We tested multiple model families and tuning regimes (zero-shot, full fine-tuning, and PEFT via LoRA), achieving an average F1 of 0.69 across the three targets. Compared to conventional compile-and-run instruments, RM-RF provides substantially lower latency and infrastructure cost while delivering competitive predictive fidelity, enabling fast, scalable feedback for large-scale test generation and RL-based code optimization.