arXiv (All)
open access
WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning
Real-world frontend development requires agents to compose reproduction, generation, modification, and repair over an evolving application state. Learning a general policy for this process requires translating heterogeneous execution feedback into task-aligned rewards and effective credit assignment
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SWE-NFI: Studying and Benchmarking Coding Agents for Non-Functional Improvements#1232812
- Executable World Models for ARC-AGI-3 in the Era of Coding Agents#158542
- AstroAgentBench: Evaluating Agentic Planning on Space Mission Planning Tasks#1156256
- CUA-Sandbox: Efficient Environments for Computer-Use Agent Reinforcement Learning#1117081
- Learning Native Reflection in Unified Models with Interleaved Reinforcement Learning#1135349
Record · ID 1387391
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive