arXiv Machine Learning
Requirement-Based Testing: Enhancing Reinforcement Learning with Game Theory
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LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
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Synthesizing Reactive Character Behaviors for Continuous Games via Programmatic Policy Search
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Hugging Face Trending Papers
Sep 3
Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs
The paper introduces Test Cases Scaling (TCS), a two‑stage reinforcement learning framework that automatically generates sound and discriminative test cases for code‑generation large language models. Stage 1 trains a test generator to produce tests aligned with a reference solution, while Stage 2 refines the generator to produce counterexample tests that target the model’s current failure modes. Experiments on TACO and LiveCodeBench show that TCS improves pass@1 and inference‑time answer selection, and the learned generator also aids in selecting better outputs from other LLMs.