Revision-Based Learning from Interactive Experience and Feedback
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 datas...
| Authors | Jiawen Li, Chenming Ge, Alice Gao et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 October 2026 |
| Last updated at source | 20 October 2026 |
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