Inference scaling with imperfect verifiers can't help weaker LLMs match stronger ones - they'll just generate more bugs.
This paper challenges the common belief that weaker LLMs can match stronger models through inference scaling. It demonstrates that imperfect verifiers like unit tests create a fundamental limit - weaker models produce more false positives that pass tests but fail in real-world scenarios.
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https://arxiv.org/abs/2411.17501
🔍 Original Problem:
Recent research suggested that inference scaling through resampling could help weaker LLMs match stronger models' performance by repeatedly sampling until finding a solution that passes verification. However, this assumes perfect verifiers, which rarely exist in practice.
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🛠️ Solution in this Paper:
→ The researchers analyzed how imperfect verifiers (like unit tests) affect the performance gap between weaker and stronger LLMs in coding tasks.
→ They introduced a cost-benefit framework to study optimal sampling strategies, considering the negative impact of false positives.
→ They evaluated solutions on HumanEval+ and MBPP+ benchmarks with comprehensive test suites beyond standard unit tests.
→ They measured code quality metrics like naming conventions and commenting to assess impact beyond just functional correctness.
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💡 Key Insights:
→ Weaker models have higher false positive rates - solutions that pass basic tests but fail comprehensive verification
→ The optimal number of resampling attempts is often less than 10, even with infinite compute budget
→ False positive solutions tend to have lower code quality across all metrics
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📊 Results:
→ Strong correlation between model capability and false positive rate on coding benchmarks
→ At cost-benefit ratio of 4, optimal sampling attempts K ≤ 5 for all tested models
→ False positives showed 20-30% worse adherence to coding conventions
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