Title: Reassessing Reinforcement Learning Assessment: Do Benchmarks Actually Expose Methodological Weaknesses?

Abstract:

Current evaluation frameworks are ill-equipped to measure advancements in reinforcement learning (RL) applied to large language models (LLMs). Although recent studies highlight significant benchmark improvements resulting from RL, our investigation reveals a critical flaw: models trained on a benchmark’s training data perform almost identically to those trained directly on its test data. This equivalence indicates that these benchmarks are unreliable for distinguishing genuine progress.

To investigate this issue, we present a diagnostic toolkit and introduce the Oracle Performance Gap (OPG), a metric designed to measure the disparity in performance between training on a benchmark’s training split versus its test split. Through rigorous stress testing, we demonstrate that while existing RL methods achieve high scores on these benchmarks, they fail to generalize effectively across distributional shifts, varying difficulty levels, and counterfactual situations. These critical deficiencies remain hidden from current benchmark metrics. Consequently, we argue that present benchmarks are inadequate for assessing generalization capabilities and outline three fundamental principles for developing more accurate evaluation standards: ensuring sufficient difficulty, maintaining balanced evaluation, and guaranteeing distributional robustness.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC