Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput

• URL: http://arxiv.org/abs/2506.10056v1
• Date: Wed, 11 Jun 2025 17:58:21 GMT
• Title: Reward Models Enable Scalable Code Verification by Trading Accuracy for Throughput
• Abstract summary: We show that an outcome reward model (ORM) plays a crucial role in scaling verification through trading accuracy for speed.<n>We analyze the generate-prune-then-rank approach and show that it works by filtering out incorrect but highly ranked solutions.
• Score: 21.59519440154879
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The standard paradigm for solving coding tasks via large language models (LLMs) is to generate-then-rank programs, where the latter step uses a verifier in the ranking process. The growing consensus is that a comprehensive verifier (e.g., a full test suite) should be prioritized over an outcome reward model (ORM) whenever possible, with little consideration given to the trade-offs involved. We aim to challenge this assumption by systematically exploring the tradeoff between speed and accuracy. We find that ORMs play a crucial role in scaling verification through trading accuracy for speed, even when a comprehensive verifier is available. Their value becomes especially apparent when used in a generate-prune-then-rank approach, where a faster but less accurate verifier removes incorrect solutions prior to ranking -- leading to a system that is 11.65x faster while only being 8.33% less accurate than the full test suite. We analyze the generate-prune-then-rank approach and show that it works by filtering out incorrect but highly ranked solutions. These findings enable the design of scalable and accurate program ranking systems.

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