CogEvol: Towards Efficient and Reliable Learning Environment Generation

2026-08-31

AI Summary

"This study addresses the challenge of efficiently converting course outlines into high-quality learning materials, such as structured JSON slides and interactive HTML pages. It proposes a single-process workflow that integrates reinforcement learning with rule-based methods to generate content from briefs. A key innovation is the implementation of a data pipeline informed by real-world application feedback to ensure output quality. Additionally, the study introduces a novel GRPO framework that combines rule-based and visual language model rewards to prevent reward hacking. Utilizing SFT, GRPO, and Ascend accelerator, the system produces each slide in 17 seconds and each interactive page in 59 seconds on average. The approach achieves favorable benchmark scores with significantly fewer parameters than leading coding models, thereby reducing costs."

Abstract

We present CogEvol, a family of models trained specifically for Learning Environment Generation: turning a course brief into a finished learning artifact (structured-JSON slides or self-contained interactive HTML pages) in a single pass. Across 220k production requests, CogEvol completes a slide in a median of 17 seconds and an interactive page in 59, replacing minutes-long multi-turn agent scaffolding. Reliability is enforced rather than hoped for: a production-grounded data pipeline turns real failures into 53,687 verified SFT samples, and a hybrid rule-plus-VLM reward drives GRPO-based RL, hardened after we caught and fixed a reward-hacking episode that produced visually convincing but unplayable games. CogEvol-27B scores 83.7 on slide quality and 63.7 on a 500-case interactive-HTML benchmark with 26.9x fewer parameters than flagship coding models, and, in collaboration with the OpenMAIC team, serves their live production traffic. CogEvol-4B is released openly under the Apache 2.0 license at https://github.com/CogEvol/CogEvol-4B; external flagships are measured on the same suites under the identical harness. Scaffold editing cuts interactive-page generation cost by a further ~76%, and the full stack runs on domestic Ascend accelerators at application-level parity with A800 GPUs, lowering the unit cost of AI-native education at scale.

Problem

• Learning Environment Generation
• Efficiency
• Reliability

Innovation

• Learning Environment Generation
• Efficiency
• Reliability
• Hybrid Rule-Plus-VLM Reward
• Cost Reduction