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.

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct Process Flow Dialogue (PFDial) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90% accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88% with an average of 11.00%. We further evaluate models’ performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in https://github.com/KongLongGeFDU/PFDial.