CURE: A Reinforcement Studying Framework for Co-Evolving Code and Unit Take a look at Technology in LLMs

Introduction

Giant Language Fashions (LLMs) have proven substantial enhancements in reasoning and precision by reinforcement studying (RL) and test-time scaling methods. Regardless of outperforming conventional unit check era strategies, most present approaches corresponding to O1-Coder and UTGEN require supervision from ground-truth code. This supervision will increase information assortment prices and limits the size of usable coaching information.

Limitations of Present Approaches

Standard unit check era depends on:

• Software program evaluation strategies, that are rule-based and inflexible.
• Neural machine translation methods, which frequently lack semantic alignment.

Whereas current prompt-based and agentic strategies enhance efficiency, they nonetheless rely closely on labeled code for fine-tuning. This reliance restricts adaptability and scalability, notably in real-world, large-scale deployment situations.

CURE: A Self-Supervised Co-Evolutionary Method

Researchers from the College of Chicago, Princeton College, Peking College, and ByteDance Seed introduce CURE, a self-supervised reinforcement studying framework that collectively trains a code generator and a unit check generator with none ground-truth code.

CURE operates utilizing a self-play mechanism through which:

• The LLM generates each right and incorrect code.
• The unit check generator learns to tell apart failure modes and refines itself accordingly.

This bidirectional co-evolution enhances each code era and verification with out exterior supervision.

Structure and Methodology

Base Fashions and Sampling Technique

CURE is constructed on Qwen2.5-7B and 14B Instruct fashions, with Qwen3-4B used for long-chain-of-thought (CoT) variants. Every coaching step samples:

• 16 candidate code completions.
• 16 task-derived unit checks.

Sampling is carried out utilizing vLLM with temperature 1.0 and top-p 1.0. For long-CoT fashions, a response-length-aware transformation penalizes prolonged outputs, enhancing inference-time effectivity.

Reward Perform and Optimization

CURE introduces a mathematically grounded reward formulation to:

• Maximize reward precision, outlined because the chance that right code scores larger than incorrect code throughout generated unit checks.
• Apply response-based reward changes for lengthy responses to scale back latency.

Optimization proceeds through coverage gradient strategies, collectively updating the coder and unit tester to enhance their mutual efficiency.

Benchmark Datasets and Analysis Metrics

CURE is evaluated on 5 commonplace coding datasets:

• LiveBench
• MBPP
• LiveCodeBench
• CodeContests
• CodeForces

Efficiency is measured throughout:

• Unit check accuracy
• One-shot code era accuracy
• Greatest-of-N (BoN) accuracy utilizing 16 code and check samples.

Efficiency and Effectivity Good points

The ReasonFlux-Coder fashions derived through CURE obtain:

• +37.8% in unit check accuracy.
• +5.3% in one-shot code era accuracy.
• +9.0% in BoN accuracy.

Notably, ReasonFlux-Coder-4B achieves 64.8% discount in common unit check response size—considerably enhancing inference velocity. Throughout all benchmarks, these fashions outperform conventional coding-supervised fine-tuned fashions (e.g., Qwen2.5-Coder-Instruct).

Software to Industrial LLMs

When ReasonFlux-Coder-4B is paired with GPT-series fashions:

• GPT-4o-mini good points +5.5% BoN accuracy.
• GPT-4.1-mini improves by +1.8%.
• API prices are decreased whereas efficiency is enhanced, indicating an economical answer for production-level inference pipelines.

Use as Reward Mannequin for Label-Free Effective-Tuning

CURE-trained unit check mills may be repurposed as reward fashions in RL coaching. Utilizing ReasonFlux-Coder-4B’s generated unit checks yields comparable enhancements to human-labeled check supervision—enabling totally label-free reinforcement studying pipelines.

Broader Applicability and Future Instructions

Past BoN, ReasonFlux-Coder fashions combine seamlessly with agentic coding frameworks like:

• MPSC (Multi-Perspective Self-Consistency)
• AlphaCodium
• S*

These methods profit from CURE’s means to refine each code and checks iteratively. CURE additionally boosts agentic unit check era accuracy by over 25.1%, reinforcing its versatility.

Conclusion

CURE represents a big development in self-supervised studying for code era and validation, enabling giant language fashions to collectively evolve their coding and unit check era capabilities with out reliance on ground-truth code. By leveraging a co-evolutionary reinforcement studying framework, CURE not solely enhances core efficiency metrics corresponding to one-shot accuracy and Greatest-of-N choice but in addition improves inference effectivity by response-length-aware optimization. Its compatibility with present agentic coding pipelines and talent to perform as a label-free reward mannequin make it a scalable and cost-effective answer for each coaching and deployment situations.

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