arXiv — cs.AI preprintsInternational21 October 2026
Revision-Based Learning from Interactive Experience and Feedback
This is an official announcement record
Firsthand records what arXiv — cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.26636v2 Announce Type: replace Abstract: Code generation models are increasingly used to produce interactive artifacts, such as websites and user interfaces. Many properties that determine whether these interfaces are useful only become apparent through realistic interaction, making them difficult to incorporate into model training. In this paper, we introduce RELIEF (REvision-based Learning from Interactive Experience and Feedback), a method for improving code generation models using feedback on their generated interfaces. RELIEF uses a coding agent to revise these interfaces, then
Read the official announcement
Opens arxiv.org
More from arXiv — cs.AI preprints
- Navalia: Advancing Cowork Agents through Verifiable Task Synthesis and Long-Horizon Post-Training21 October 2026
- Large language model and reinforcement learning based autonomous construction and progress updates21 October 2026
- Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models21 October 2026
- MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring21 October 2026
- DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution21 October 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.