Paper page - CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test

Source: https://huggingface.co/papers/2605.23491

Abstract

CoSPlay is a GT-free framework that jointly improves code generation and unit test quality through cooperative self-play, achieving competitive performance without ground-truth unit tests.

Recently,Reinforcement Learning with Verifiable Rewards(RLVR) andTest-Time Scaling(TTS) have advanced LLMcode generationthrough executable verification. YetGround-Truth Unit Tests(GT UTs) remain a bottleneck: SOTA RLVR methods require them for costly training, while existing TTS methods lose competitiveness without them. This motivates GT-free TTS, where existing methods directly use self-generated UTs to refine and select code candidates. Yet such UTs are often noisy or spuriously coupled with wrong code, and UT quality in turn cannot be validated without reliable code. The key challenge is therefore to jointly improve both. To this end, we present CoSPlay, a GT-free, training-free framework that jointly improves codes and UTs throughcooperative self-play. It first explores diverse solution ideas and identifies their potential failure modes to produce discriminative UT ideas. It then usesbidirectional pass-count signalsfrom the Code-UT execution matrix to iteratively prune or fix weak codes and refresh or replace unreliable UTs, letting the two pools co-evolve. Finally, when multiple codes remain tied at the highest pass count, it picks the final code from the largestoutput-consensus cluster, since correct codes agree on the same inputs while wrong codes diverge. Experiments on four challenging benchmarks show that CoSPlay onQwen2.5-7B-Instructimproves average BoN from 22.1% to 33.2% and UT accuracy from 14.6% to 78.3%, matching or surpassing the RLVR modelCURE-7B. When applied toCURE-7B, it further improves BoN by 5.7%. CoSPlay also generalizes across diverse backbones and outperforms GT-free TTS baselines under comparable token budgets, with continued gains as the budget scales up. These results suggest a scalable inference strategy for competitivecode generationwithout any GT data.

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