DreamPRM-Code: Function-as-Step Process Reward Model with Label Correction for LLM Coding
Process Reward Models (PRMs) have become essential for improving Large Language Models (LLMs) via test-time scaling, yet their effectiveness in coding remains limited due to the lack of meaningful step decompositions in code and the noise of Monte-Carlo-generated partial labels. We propose DreamPRM-Code, a coding-focused PRM that treats functions as reasoning steps using a Chain-of-Function prompting strategy to induce modular code generation, enabling PRM training and application analogous to mathematical reasoning tasks. To address label noise, DreamPRM-Code introduces a meta-learning-based correction mechanism that leverages clean final-solution unit-test labels and performs bi-level optimization to refine intermediate labels. Applying on test-time scaling, DreamPRM-Code achieved state-of-the-art performance on LiveCodeBench with 80.9 pass@1 rate, surpassing OpenAI o4-mini.
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Tasks
Mathematical ReasoningCode GenerationResults from the Paper
| Rank | Task | Dataset | Model | Metrics |
|---|---|---|---|---|
| #26 | Code Generation | LiveCodeBench | DreamPRM-Code | Pass@1: 80.9 |
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