Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models

• URL: http://arxiv.org/abs/2601.06911v1
• Date: Sun, 11 Jan 2026 13:34:44 GMT
• Title: Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models
• Authors: Shaoning Sun, Mingzhu Cai, Huang He, Bingjin Chen, Siqi Bao, Yujiu Yang, Hua Wu, Haifeng Wang,
• Abstract summary: We show that RL-friendly models exhibit intra-class compactness and inter-class separation in their probability assignments to correct vs. incorrect responses.<n>Experiments across six mathematical benchmarks show consistent improvements across all model families, with gains up to 5.9 points on AIME24.
• Score: 50.99097734404912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language model families exhibit striking disparity in their capacity to benefit from reinforcement learning: under identical training, models like Qwen achieve substantial gains, while others like Llama yield limited improvements. Complementing data-centric approaches, we reveal that this disparity reflects a hidden structural property: \textbf{distributional clarity} in probability space. Through a three-stage analysis-from phenomenon to mechanism to interpretation-we uncover that RL-friendly models exhibit intra-class compactness and inter-class separation in their probability assignments to correct vs. incorrect responses. We quantify this clarity using the \textbf{Silhouette Coefficient} ($S$) and demonstrate that (1) high $S$ correlates strongly with RL performance; (2) low $S$ is associated with severe logic errors and reasoning instability. To confirm this property, we introduce a Silhouette-Aware Reweighting strategy that prioritizes low-$S$ samples during training. Experiments across six mathematical benchmarks show consistent improvements across all model families, with gains up to 5.9 points on AIME24. Our work establishes distributional clarity as a fundamental, trainable property underlying RL-Friendliness.

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