Group-relative policy optimization (GRPO) provides a stable recipe for outcome-based RL on language model reasoning, but its credit assignment is coarse: it broadcasts a single trajectory advantage to every token, regardless of how individual steps contribute to the outcome. We call this the uniform-broadcast failure mode and address it with RLPgR, a dependency-aware rescaling of the existing GRPO advantage. RLPgR parses each rollout into a Progress Dependency Graph (PDG), where each node groups a contiguous span of steps into a milestone-level unit with a locally validatable artifact, and directed edges connect a node to the artifacts it depends on. Using the PDG, RLPgR rescales the group-relative advantage by a small, bounded, sign-preserving factor at the node level. This preserves the outcome-governed rollout direction while each step’s update better reflects its dependency role, avoiding the reward-hacking drift of densely shaped process rewards. To reduce PDG construction cost, we train an open Qwen3-8B progress annotator amortizing a one-time proprietary bootstrap. Across model families and sizes on five math reasoning benchmarks, RLPgR is competitive with strong outcome-RL and process-RL baselines under matched training budget. Three controlled studies (scaling-strength sensitivity, segment-level baselines, and PDG label perturbation) support the view that the gain comes from structured credit assignment rather than incidental tuning.

Reward models (RMs) are the surrogate objectives in reinforcement learning from human feedback (RLHF), and their scores directly steer policy optimization. We show that standard RM training is vulnerable in data subsets where response quality depends only weakly on the context: such instances encourage the RM to ignore the context, leading to context neglect and degraded accuracy. To address this failure mode, we propose Distribution-Aware Reward Modeling (DARM), which augments the RM objective with a conditional mutual information regularizer that maximizes context and the predicted reward conditioned on the response. By explicitly preserving the sensitivity of reward signals to the prompting context, DARM reduces over-reliance on response-only features and improves robustness to contextual variation. Extensive experiments across in-distribution and out-of-distribution settings show that DARM trained RMs deliver more accurate and consistent scoring than strong baselines. We further evaluate its downstream impact in RLHF, where DARM produce better aligned policies. We also demonstrate the necessity of each DARM design component and the impact of key parameters on performance through ablation experiments.