SAGE combines supervised fine-tuning and GRPO reinforcement learning to generate CCFG grammars from competitive programming specs, achieving 96.66% set-based validity and 80.67% set-based effectiveness on 240 held-out problems.
RePair: Automated program repair with process-based feedback,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
SAGE:Specification-Aware Grammar Extraction for Automated Test Case Generation with LLMs
SAGE combines supervised fine-tuning and GRPO reinforcement learning to generate CCFG grammars from competitive programming specs, achieving 96.66% set-based validity and 80.67% set-based effectiveness on 240 held-out problems.