TSP reframes secure code generation as a tree-structured self-play process that supplies dense on-policy signals at vulnerability-prone nodes, yielding higher security pass rates and cross-language generalization than SFT or unstructured self-play.
Think outside the code: Brainstorming boosts large language models in code generation
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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UNVERDICTED 2representative citing papers
PGS generates property-oriented, structurally minimal feedback from high-level program properties to refine LLM code, yielding up to 13.4% pass@1 gains and 1.4-1.6x higher bug-fix rates than prior TDD and debugging baselines.
citing papers explorer
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Learn from Your Mistakes: Tree-like Self-Play for Secure Code LLMs
TSP reframes secure code generation as a tree-structured self-play process that supplies dense on-policy signals at vulnerability-prone nodes, yielding higher security pass rates and cross-language generalization than SFT or unstructured self-play.
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Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback
PGS generates property-oriented, structurally minimal feedback from high-level program properties to refine LLM code, yielding up to 13.4% pass@1 gains and 1.4-1.6x higher bug-fix rates than prior TDD and debugging baselines.