# Rubric-to-Code Credit Assignment：面向强化学习的评分细则到代码信用分配框架

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-28 08:00
- AIHOT 分数：45
- AIHOT 链接：https://aihot.virxact.com/items/cmtgmdkaf02zzrochmb02hxw0
- 原文链接：https://arxiv.org/abs/2608.27906

## AI 摘要

研究提出 Rubric-to-Code Credit Assignment（RCCA），一种将评分细则级功能反馈转化为代码局部优化信号的强化学习框架，以改善交互式网页应用生成中的信用分配问题。

## 正文

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 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, 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.