Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement
  Learning for General LLM Reasoning
Authors: name: Yang Zhou, name: Sunzhu Li, name: Shunyu Liu, name: Wenkai Fang, name: Jiale Zhao, name: Jingwen Yang, name: Jianwei Lv, name: Kongcheng Zhang, name: Yihe Zhou, name: Hengtong Lu, name: Wei Chen, name: Yan Xie, name: Mingli Song

Recent advances in Large Language Models (LLMs) have underscored the
potential of Reinforcement Learning (RL) to facilitate the emergence of
reasoning capabilities. Despite the encouraging results, a fundamental dilemma
persists as RL improvement relies on learning from high-quality samples, yet
the exploration for such samples remains bounded by the inherent limitations of
LLMs. This, in effect, creates an undesirable cycle in which what cannot be
explored cannot be learned. In this work, we propose Rubric-Scaffolded
Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework
designed to break the exploration bottleneck for general LLM reasoning.
Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit
scaffolding for exploration during rollout generation, where different rubrics
are provided as external guidance within task instructions to steer diverse
high-quality responses. This guidance is gradually decayed over time,
encouraging the model to internalize the underlying reasoning patterns; (2)
verifiable rewards for exploitation during model training, where we can obtain
robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL
on general reasoning tasks. Extensive experiments demonstrate the superiority
of the proposed RuscaRL across various benchmarks, effectively expanding
reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL
significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500,
surpassing GPT-4.1. Furthermore, our fine-tuned variant on
Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading
LLMs including OpenAI-o3.