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

ACL ARR 2026 January Submission4924 Authors

Keywords: RL-Friendliness, Distributional Clarity, Reinforcement Learning with Verifiable Rewards, Large Language Models

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: **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 **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.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: calibration/uncertainty, reinforcement learning

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 4924