AI Research Analysis
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 04-mini.
Executive Impact at a Glance
DreamPRM-Code redefines how LLMs tackle coding challenges, offering a robust framework for superior performance and more efficient development cycles in enterprise AI applications.
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Pass@1 Rate Achieved
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Performance Gain over baselines
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Reduced PRM Evaluation Steps
Deep Analysis & Enterprise Applications
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Understanding Process Reward Models
Process Reward Models (PRMs) evaluate intermediate reasoning steps of Large Language Models (LLMs) to provide fine-grained feedback, crucial for tasks like mathematical reasoning and code generation. They offer a more granular supervision signal than outcome-based rewards, enabling more effective reinforcement learning and test-time scaling. This paper highlights that while successful in mathematics, PRMs face challenges in coding due to ill-defined steps and noisy labels.
Streamlining Code Generation with CoF
DreamPRM-Code introduces a novel Chain-of-Function (CoF) prompting strategy. Instead of using lines of code, CoF treats each function as a distinct reasoning step. This encourages LLMs to generate modular, well-structured programs, making the intermediate steps more meaningful and aligned with human software engineering practices. This structured decomposition enables standard PRM training and inference pipelines to be directly adapted for coding tasks, addressing the challenge of ill-defined steps in code.
Purifying Noisy Labels for Robust PRMs
A key innovation in DreamPRM-Code is its meta-learning-based label correction mechanism. PRMs are typically trained on noisy Monte-Carlo-generated pseudo-labels for intermediate steps. DreamPRM-Code leverages clean, final-solution unit-test labels as meta-supervision to automatically refine these noisy intermediate labels. Through a bi-level optimization framework, the model adaptively corrects label noise, leading to more robust PRM training and improved performance in downstream tasks.
80.9%
Pass@1 Rate on LiveCodeBench, Surpassing OpenAI 04-mini
Enterprise Process Flow
Problem Specification
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Chain-of-Function Prompting
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High-Level Function Generation (e.g., `main()`)
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Helper Function Generation (e.g., `dijkstra()`, `build_graph()`)
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Modular Code Solution
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LiveCodeBench: Benchmarking Real-world Impact
DreamPRM-Code's effectiveness was rigorously evaluated on LiveCodeBench (Jain et al., 2025), a comprehensive and contamination-free benchmark for code generation. This benchmark utilizes newly released programming problems from platforms like LeetCode and AtCoder, ensuring relevance and preventing data overlap. By training on problems before 2024-08-01 and testing on those published after 2025-02-01, a strict temporal separation was maintained. The model’s 80.9% pass@1 rate demonstrates its strong performance in real-world coding scenarios, validating the benefits of both Chain-of-Function prompting and meta-learning label correction.
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