Iterative self-repair improves LLM code pass rates by 4.9-17.1 pp on HumanEval and 16-30 pp on MBPP across seven models, with gains concentrated early and syntax errors easier to fix than logical ones.
Reflexion: Language agents with verbal reinforcement learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
How Many Tries Does It Take? Iterative Self-Repair in LLM Code Generation Across Model Scales and Benchmarks
Iterative self-repair improves LLM code pass rates by 4.9-17.1 pp on HumanEval and 16-30 pp on MBPP across seven models, with gains concentrated early and syntax errors easier to fix than logical ones.