Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model

Researchers present a boundary-aware Curriculum Reinforcement Learning approach that improves large language model reasoning capacity beyond what standard RLVR methods achieve. Testing across Qwen, Llama, and DeepSeek models shows 9.8 percentage point improvements in pass@256 scores over base models, suggesting a more scalable path for continuous LLM advancement.

This research addresses a fundamental limitation in current reinforcement learning approaches for large language models. Standard RLVR methods optimize sampling efficiency within the existing capability distribution of base models, improving single-attempt performance metrics while leaving the underlying reasoning capacity largely unchanged. The boundary-aware Curriculum RL framework introduces a three-stage methodology that identifies where models currently fail, applies targeted guidance to expand capabilities at these failure points, and consolidates improvements through reinforcement learning.

The work builds on growing recognition that efficiency gains alone do not translate to genuine capability expansion. By using pass@k sampling as a diagnostic tool for locating reasoning boundaries, the researchers create a systematic approach to push beyond existing limitations. This represents a meaningful departure from treating all trajectories equally during training.

For the AI development industry, these results carry practical significance. The demonstrated improvements across multiple model families—Qwen, Llama, and DeepSeek—suggest the approach generalizes well across different architectures. The pass@256 metric serves as an empirical proxy for reasoning capacity boundaries, offering a measurable framework for tracking genuine capability improvements rather than sampling optimization.

Looking forward, this methodology could influence how AI labs structure training pipelines for next-generation models. The scalability claims suggest the approach doesn't require prohibitive computational resources, making it accessible to various research groups. The implications extend beyond academic optimization; more efficient pathways to reasoning capability improvements could accelerate the practical deployment timeline for more capable systems across industries.

• →Curriculum RL with boundary awareness improves pass@256 scores by 9.8 points over base models and 10.3 points over vanilla RLVR
• →Standard RLVR reallocates sampling probabilities within existing model capabilities rather than expanding reasoning boundaries
• →The three-stage approach identifies failure boundaries, applies targeted guidance, and consolidates new reasoning patterns
• →Results demonstrate generalization across Qwen, Llama, and DeepSeek model families
• →Pass@k sampling provides a measurable framework for tracking genuine capability improvements beyond efficiency gains