Study: Benchmark Optimization Doesn't Prove General Coding Ability
A recent study published on arXiv (2608.13566) questions the validity of relying on scores from coding benchmarks such as SWE-bench and LiveCodeBench as indicators of overall coding skills. The researchers contend that focusing on these benchmarks reflects only task-specific abilities, leading to a disconnect between scores and actual general coding proficiency. They developed a benchmark suite based on Django for their case study and assessed foundation models and checkpoints that were fine-tuned on SWE-bench data. Results indicated that the rankings from these benchmarks do not consistently translate across tasks; post-trained checkpoints showed minimal cross-task transfer, and optimizing for SWE-bench did not significantly enhance performance on other tasks or LiveCodeBench. Additionally, fine-tuning on specific Django aspects did not yield transferable results. The authors conclude that a limited number of benchmarks cannot adequately assess varied models, highlighting the necessity for more comprehensive evaluation approaches, which is crucial for AI researchers, developers, and coding model users.
Key facts
- The paper is titled 'Don't Claim Benchmark-Oriented Optimization Improves General Coding Capability -- Diverse Evaluation Is Required'.
- It is available on arXiv with ID 2608.13566.
- The authors created a Django-based case study benchmark suite for evaluation.
- They evaluated foundation models and checkpoints post-trained on SWE-bench trajectories.
- Benchmark rankings frequently fail to generalize across tasks.
- Post-trained checkpoints show little cross-task transfer.
- SWE-bench optimization yields limited or no gains on their tasks or on LiveCodeBench.
- Fine-tuning on individual Django modalities fails to transfer.
- The paper concludes that a small number of benchmarks is insufficient for evaluating diverse models.
Entities
Institutions
- arXiv