GSPO

SCOPE-RL framework densifies reward signals in RLVR to improve reasoning accuracy and token efficiency

SCOPE-RL introduces a two-stage reinforcement learning framework that addresses sparse reward limitations in RLVR by adding prefix-decomposed sub-question rewards before success and correctness-gated process-shape rewards after success. Applied to Qwen3-8B-Instruct on math reasoning datasets, the method improves average accuracy by up to 11.2 percentage points and reduces reasoning tokens by up to 27.1% over outcome-only GRPO. The gains generalize across GSPO and a smaller Qwen3-0.6B model, suggesting reward-signal densification is broadly complementary to existing RLVR advances. Code and data are publicly released.