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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes Test Cases Scaling (TCS), a two-stage reinforcement learning framework designed to automatically generate sound and adversarial test cases for code LLMs. The authors report improvements in pass@1 and answer selection metrics on TACO and LiveCodeBench benchmarks, but the work is unreviewed and lacks formal statistical comparison, effect sizes, and external validation.
Preprint. Large language models trained for code generation; evaluation on coding problem-solving benchmarks (TACO and LiveCodeBench).. Intervention: Test Cases Scaling (TCS): two-stage reinforcement learning framework. Stage 1 generates tests consistent with reference solution using rolling policy-aligned buffer. Stage 2 restricts buffer to current failure modes and learns counterexamp….
TCS improves pass@1 performance on TACO and LiveCodeBench (exact figures not provided in abstract) Two-stage approach: Stage 1 generates tests consistent with reference solution; Stage 2 generates counterexample tests from current failure modes Learned test generator enables effective selection among other LLM outputs
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A novel machine-learning framework tested on coding benchmarks without peer review; demonstrates feasibility and initial results but requires independent validation and clinical or practical deployment evidence.
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Reinforcement learning (RL) has substantially advanced code generation with large language models (LLMs) through executable feedback. The feedback for coding problems mainly comes from specific test cases, where high-quality test cases are often scarce since they should be both sound and discriminative. We thus turn to study the auto-generation of test cases using the learned model. We find this is naturally an adversarial RL problem: the model is expected to generate effective test cases as counterexamples, depending on the solver's current failure modes. We propose Test Cases Scaling (TCS), a two-stage RL framework for effective test generation. Both stages train a test generator from a rolling policy-aligned buffer: Stage 1 generates tests consistent with the reference solution, and Stage 2 restricts the buffer to current failure modes and learns counterexample tests. Across TACO and LiveCodeBench, TCS improves both pass@1 and inference-time answer selection according to generated tests. We find the learned test generator also enables effective selection among other LLM outputs.
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