CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning

ACL ARR 2026 January Submission3731 Authors

Keywords: Reinforcement Learning, Unit Test Generation, Large Language Model

Abstract:

Code verifiers play a critical role in post-verification for LLM-based code generation, yet existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.
While reinforcement learning (RL) offers a promising alternative by optimizing models through execution-driven rewards without labeled supervision, our preliminary results show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
We first theoretically analyze showing that branch coverage, sample difficulty, syntactic and functional correctness can be jointly modeled as RL rewards, where optimizing these signals can improve the reliability of unit-test–based verification.
Guided by this analysis, we design syntax- and functionality-aware rewards and further propose branch- and sample-difficulty–aware RL using exponential reward shaping and static analysis metrics.
With this formulation, CVeDRL achieves state-of-the-art performance with only 0.6B parameters, yielding up to 28.97\% higher pass rate and 15.08\% higher branch coverage than GPT-3.5, while delivering over 20× faster inference than competitive baselines.
Code is available at https://anonymous.4open.science/r/CVeDRL-DF1A/

Paper Type: Long

Research Area: Code Models

Research Area Keywords: Generation,Language Modeling,NLP Applications

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 3731