Summary of Learning Code Preference Via Synthetic Evolution, by Jiawei Liu et al.
Learning Code Preference via Synthetic Evolution
by Jiawei Liu, Thanh Nguyen, Mingyue Shang, Hantian Ding, Xiaopeng Li, Yu Yu, Varun Kumar, Zijian Wang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the challenge of assessing code generation based on well-formed properties and aligning it with developer preferences. The researchers propose CodeFavor, a framework for training pairwise code preference models from synthetic evolution data, including code commits and code critiques. They introduce CodePrefBench, a benchmark comprising 1364 rigorously curated code preference tasks covering three verifiable properties: correctness, efficiency, and security, along with human preference. The evaluation shows that CodeFavor improves the accuracy of model-based code preferences by up to 28.8%. Meanwhile, CodeFavor models can match the performance of models with 6-9x more parameters while being 34x more cost-effective. This paper also highlights the prohibitive costs and limitations of human-based code preference: despite spending 23.4 person-minutes on each task, 15.1-40.3% of tasks remain unsolved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about teaching computers to understand what makes good code. The authors want to know how humans decide if code is good or bad and if computers can learn this too. They created a way for computers to learn from data about code and then tested it against real human preferences. The results show that the computer method works better than expected, even with less training data. This is important because computers could help developers write better code faster. However, humans have limitations when evaluating code themselves – they can spend a lot of time on each task but still not get it right. |
