Learning the Loop, Not Just the Page: Execution-Grounded Loop Learning for Web Generation

arXiv:2610.11543 · cs.CL · Submitted 2026-10-08 · Read on arXiv

cs.CL

Submitted: 2026-10-08

Updated: 2026-10-08

Code: https://github.com/IIGROUP/WebLoop

Terminology

• GRPO — 15× in this paper · explained in 6 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Reinforcement Learning — 9× in this paper · explained in 4 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Credit assignment — 6× in this paper · explained in 3 episodes
• Trace — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Actor — 4× in this paper · explained in 3 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Interaction Contract Graph — 3× in this paper
• Commentary — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Multimodal Inputs — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• GRPO Objective — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• State Management — 1× in this paper · explained in 3 episodes
• Two-Stage Optimization — 1× in this paper · explained in 2 episodes
• Verifiability — 1× in this paper · explained in 2 episodes

Sources

• WebGrader: Training LLMs for Web Development with Self-Evolving Programmatic Grader
• WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
• Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset
• WebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
• LLM Critics Help Catch LLM Bugs
• WebRISE: Requirement-Induced State Evaluation for MLLM-Generated Web Artifacts
• Self-Taught Evaluators
• Critique-RL: Training Language Models for Critiquing through Two-Stage Reinforcement Learning
• Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback
• FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering