Don't Claim Benchmark-Oriented Optimization Improves General Coding Capability — Diverse Evaluation Is Required
Что произошло
Источник arXiv cs.LG зафиксировал сигнал: arXiv:2608.13566v1 Announce Type: new
Abstract: Post-training papers, model cards, and blog posts often treat scores on a small set of coding benchmarks (e.g., SWE-bench and LiveCodeBench) as evidence of broad coding capability, both for research artifacts and user-facing systems. We argue that optimization for these benchmarks leads to measuring task-specific performance, creating a meaning gap between measured scores and claims of general coding ability. We examine this gap with a Django-based case study benchmark suite we create.
Evaluating foundation models and checkpoints post-trained on SWE-bench trajectories, we find that benchmark rankings frequently fail to generalize. Post-trained checkpoints show little cross-task transfer, and SWE-bench optimization yields limited or no gains on our tasks or on LiveCodeBench. Similarly, fine-tuning on individual Django modalities fails to transfer.
We conclude that a small number of benchmarks is insufficient for evaluating diverse models under benchmark optimization pressure. We encourage the community to use differentiated evaluation – holistic assessment for frontier models, multi-task suites for research, and human-in-the-loop studies for narrow task applications. Finally, we argue for creating a capability taxonomy and sustained benchmark maintenance, rather than one-off benchmark releases. Without reliable evaluation standards, engineers and researchers using LLMs and agents have to rely on insufficient evidence to make research, development, and deployment decisions.
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Что подтверждено
| Punkt | Detail |
|---|---|
| Платформа | arxiv |
| Источник | arXiv cs.LG |
| Проверка | https://arxiv.org/ |
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Источник: arXiv cs.LG – https://arxiv.org/abs/2608.13566; проверка: https://arxiv.org/