Newest papers

Revision-Based Learning from Interactive Experience and Feedback

Jiawen Li, Chenming Ge, Alice Gao, Rishab Vempati, Yi-Hao Peng, Jason Wu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26636 · cited by 0 works (OpenAlex) · open-access PDF

German

Viele Eigenschaften, die bestimmen, ob diese Schnittstellen nützlich sind, werden nur durch realistische Interaktion sichtbar, was sie schwierig macht, in das Modelltraining einzubeziehen. In diesem Papier führen wir RELIEF (Revision-based Learning from Interactive Experience and Feedback) ein, eine Methode zur Verbesserung von Code-Generationsmodellen mithilfe von Feedback auf ihren erzeugten Schnittstellen. RELIEF verwendet einen Codierungsagent, um diese Schnittstellen zu überprüfen, und integriert dann die Verbesserungen in ihre ursprünglichen Generationsspuren, um die Überwachung für Fein-Tuning zu erzeugen. Um RELIEF zu bewerten, sammelten wir einen Datensatz von 200 synthetisch erzeugten Websites, gepaart mit 355 Interaktionsaufzeichnungen

Machine translation (Meta M2M100); the English original follows.

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 incorporates the improvements into their original generation traces to produce supervision for fine-tuning. To evaluate RELIEF, we collected a dataset of 200 synthetically generated websites paired with 355 interaction recordings and accompanying feedback from 24 users. We used critiques derived from code inspection, automated interaction, explicit human feedback, and human interaction recordings to fine-tune variants of two code generation models. Our results show that feedback effectiveness varies with base model capabilities, with critiques inferred from human interaction recordings leading to the largest usability improvements overall under our tested models.

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Topics: Computer science, Usability, Human–computer interaction, Code (set theory), Coding (social sciences), User experience design

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