Rubric-to-Code Credit Assignment for Reinforcement Learning

2026-08-28

AI Summary

本文提出Rubric-to-Code Credit Assignment (RCCA)方法，通过将功能性反馈转化为局部优化信号来改进交互式Web应用的代码生成，显著提高了模型在多个基准测试上的表现。

Abstract

Interactive web application generation requires models to produce usable HTML, CSS, and JavaScript applications from natural language requests. Unlike conventional code generation, application quality depends on multiple user-facing functional requirements, each often tied to localized code regions such as event handlers, state updates, DOM fragments, or CSS selectors. Standard GRPO collapses these structured outcomes into a single sequence-level reward and applies the resulting advantage uniformly to all tokens, weakening credit assignment. We propose \textbf{Rubric-to-Code Credit Assignment} (RCCA), a reinforcement learning framework that converts rubric-level functional feedback into localized optimization signals over generated code. RCCA builds training tasks around explicit functional rubrics, uses a hierarchical reward to separate format, source-code, runtime, and functional failures, and aligns evaluator-generated textual attributions with responsible code spans and generated tokens. The resulting model, \textbf{Ling-RCCA-Flash}, scores 41.25 on MiniAppBench, improving Ling-3.0-Flash by 32.20 points and slightly surpassing Claude Opus 4.5. It also reaches 76.19 on ArtifactsBench, improving the SFT model by 4.48 points and establishing a new top score under the official ArtifactsBench leaderboard setting by surpassing the GPT-5 score by 3.64 points, suggesting transferable implementation-level gains.

Problem

• Reinforcement Learning
• Interactive Web Application
• Code Generation
• Functional Requirements
• Credit Assignment

Innovation

• Reinforcement Learning
• Credit Assignment
• Hierarchical Reward
• Functional Feedback
• Code Generation