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Life sciences · Preprint
arXiv · September 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes ERPO, a reinforcement learning method designed to enable test-time learning for code generation by using probe-driven reward signals and entropy regularization to prevent reward hacking. The approach shows reported improvements in pass@1 and pass@k metrics on coding benchmarks, but the work is unreviewed and lacks quantified effect sizes, baseline comparisons, or independent validation.
Preprint. Code generation models on coding benchmarks. Intervention: Entropy-Regularized Rank-Masked Policy Optimization (ERPO) with probe-driven test-time reinforcement learning.
ERPO substantially improves pass@1 and pass@k on coding benchmarks in both in-domain adaptation and zero-shot transfer settings Probe Consensus Reward (PCR) constructs behavioral agreement signals from candidate program execution on synthesized probe inputs Entropy-regularized rank masking converts low PCR scores into conservative negative updates to prevent policy drift
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A novel algorithmic approach to an unvalidated problem (test-time RL for code generation) demonstrated on benchmarks without peer review, showing promising technical results but lacking independent verification or clinical/practical implementation data.
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Existing methods for test-time reinforcement learning (TTRL) derive rewards from answer-level self-voting on unlabeled test-time tasks with canonical answers, but this breaks down for code generation because programs cannot be compared by surface form and therefore do not directly provide a usable training signal. To make TTRL applicable to code generation, we propose probe-driven TTRL, which constructs output-free probe inputs from the problem statement, executes candidate programs on these probes, and defines a Probe Consensus Reward (PCR) from the resulting behavioral agreement. PCR provides a behavioral training signal for open-vocabulary programs, but it is not a fully reliable verifier and remains susceptible to reward hacking through spurious consensus. We therefore introduce Entropy-Regularized Rank-Masked Policy Optimization (ERPO), which converts low PCR into conservative negative updates through rank masking and controls policy drift with an entropy ceiling. On coding benchmarks, ERPO substantially improves pass@1 and pass@k in both in-domain adaptation and zero-shot transfer.
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