Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language models. Despite its effectiveness, how response-level rewards translate into token-level probability changes remains poorly understood. We introduce a discriminator view of RLVR updates, showing that the policy-gradient update direction implicitly acts as a linear discriminator over token-gradient vectors and thereby determines which token probabilities are increased or decreased during learning. Under standard sequence-level RLVR, this discriminator is constructed from positive- and negative-side centroids formed by advantage-weighted averaging of token-gradient vectors. However, such centroid construction can be dominated by shared high-frequency patterns, such as formatting tokens, diluting sparse yet discriminative directions that better distinguish high-reward responses from low-reward ones. To address this limitation, we propose $\textbf{DelTA}$, a discriminative token credit assignment method that estimates token coefficients to amplify side-specific token-gradient directions and downweight shared or weakly discriminative ones. These coefficients reweight a self-normalized RLVR surrogate, making the effective side-wise centroids more contrastive and thereby reshaping the RLVR update direction. On seven mathematical benchmarks, DelTA outperforms the strongest same-scale baselines by 3.26 and 2.62 average points on Qwen3-8B-Base and Qwen3-14B-Base, respectively. Additional results on code generation, a different backbone, and out-of-domain evaluations further demonstrate the generalization ability of DelTA.

翻译：基于可验证奖励的强化学习（RLVR）已成为提升大语言模型推理能力的核心技术。尽管其效果显著，但响应级奖励如何转化为令牌级概率变化的问题仍不明确。我们引入了一种RLVR更新的判别器视角，证明策略梯度更新方向隐式地扮演了令牌梯度向量的线性判别器角色，从而决定了学习过程中哪些令牌概率被提升或降低。在标准序列级RLVR中，该判别器由正负两侧质心构建而成，这些质心通过对令牌梯度向量进行优势加权平均得到。然而，这种质心构建可能被共享的高频模式（如格式令牌）主导，从而稀释了能更好区分高奖励响应与低奖励响应的稀疏判别性方向。为解决这一局限，我们提出$\textbf{DelTA}$——一种判别性令牌信用分配方法，通过估计令牌系数来放大特定侧的令牌梯度方向，同时降低共享或弱判别性方向的影响。这些系数重新加权自归一化的RLVR代理函数，使有效的侧向质心更具对比性，从而重塑RLVR更新方向。在七个数学基准测试中，DelTA在Qwen3-8B-Base和Qwen3-14B-Base上分别平均超越最强同规模基线3.26分和2.62分。代码生成、不同骨干网络及域外评估的附加结果进一步证明了DelTA的泛化能力。