SCOPE-RL 优化推理路径：成功之前与成功之后

Jul 16, 2026

Reinforcement learning with verifiable rewards (RLVR) improves LLMs using sparse terminal rewards that reliably confirm whether a trajectory succeeds, yet fail to provide feedback about the reasoning path. Before success, progress on hard problems yields no useful signal; after success, endpoint rewards cannot separate well-organized correct reasoning from redundant or locally flawed steps. SCOPE-RL introduces a two-stage framework to densify the reward signal while keeping GRPO updates, boosting accuracy up to 11.2 pp and reducing reasoning tokens by up to 27.1%.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages21

Reading Time53 min

Abstract

Introduction

SCOPE-RL: two-stage RLVR framework

Step-Quality Evaluation Protocol

Experimental results and contributions

SCOPE-RL: Optimizing Reasoning Paths Before and After Success Xiaojian Liu 1 ,2∗, Han Xu2∗†, Jianqiang Xia2 , Zhixuan Li2 , Ke Xu2 , Yiwei Dai2 , Xinran Chen2 , Changwo Wu2 , Yuchen Li2 1 Baidu Inc. 2 Shandong University [liux83596@gmail.com](liux83596@gmail.com), [xhbj66@gmail.com](xhbj66@gmail.com) arXiv :2607 . 1 1506v 1 [ cs .LG] 13 Jul 2026 Abstract Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards. This sparse anchor reliably verifies whether a trajectory succeeds but provides no direct feedback on the reasoning path that produced it. Before success, prerequisite progress on hard problems receives no reward signal; after success, outcome rewards cannot distinguish well-organized correct trajectories from redundant or locally flawed ones. We introduce SCOPE-RL (Scaffolded Chain Optimization with Process Efficiency), a twostage framework that densifies this anchor while retaining the GRPO update: Adaptive Scaffolded RL adds prefix-decomposed verifiable rewards on answer-hidden sub-question chains before success, and Quality-Aware Process RL applies correctness-gated processshape rewards to refine correct trajectories after success. An expert-validated Step-Quality Evaluation Protocol evaluates useful-step density, error localization, and token efficiency beyond final-answer accuracy. On Qwen3-8BInstruct trained on DAPO-Math and Big-Math, SCOPE-RL improves average accuracy by up to 11.2 pp and reduces reasoning tokens by up to 27.1% over outcome-only GRPO; the gains hold under GSPO and on Qwen3-0.6B-Instruct, indicating that reward-signal densification is complementary to policy-update-level RLVR advances.Code and data are available at [https:](https:)//[github.com/tokencraft-lab/SCOPE-RL](github.com/tokencraft-lab/SCOPE-RL). 1 Introduction Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse terminal rewards, typically final-answer correctness (Guo et al., 2025 ; Shao et al., 2024 ; Yu et al., 2025 ; Zheng et al., 2025 ; Liu et al., 2025) . This terminal anchor reliably certifies success but provides no ∗Equal contribution. †Corresponding author. direct feedback on the reasoning path, leading to two failure modes usually treated separately: training stalls on hard problems where outcome-only rollouts rarely succeed (Pang et al., 2025 ; Shi et al., 2025 ; Ma et al., 2025), and models produce correct but inefficient reasoning on problems they can already solve (Zhou et al., 2026 ; Rakotonirina et al., 2026 ; He et al., 2025) . Before success, prerequisite progress is invisible unless it reaches the final answer, so near-misses provide little signal; after success, endpoint rewards cannot distinguish wellorganized correct trajectories from redundant or locally flawed ones. Two diagnostic probes provide evidence for this interpretation (Figure 1; details in Appendix C): scaffolded prompts reveal prerequisite signal that endpoint rewards leave unexposed, and among final-answer-correct rollouts outcomeonly GRPO increases response length while retaining low-value content. This sparse-anchor view reframes prior work as addressing different consequences of the same anchor: exploration-oriented methods reshape inputs, rollouts, or updates on hard problems (Parasharet al., 2025 ; Pang et al., 2025 ; Bae et al., 2026 ; Shi et al., 2025 ; Chih-Yao Chen et al., 2026 ; Simonds and Yoshiyama, 2025 ; Qu et al., 2026 ; Liao et al., 2026 ; Zhang et al., 2025 ; Liu et al., 2026 ; Yanet al., 2025), efficiency methods reduce overthinking (Hou et al., 2025 ; He et al., 2025 ; Rakotonirina et al., 2026), and process-supervision methods train PRMs, derive implicit rewards, or reassign dense feedback (Lightman et al., 2023 ; Wang et al., 2024 ; Cui et al., 2025 ; Rezaei et al., 2026 ; Yao et al., 2026 ; Cheng et al., 2025) . Less explored is how the rule-verifiable signal itself can be densified through independently verified sub-ans

FAQs

为什么仅使用终点可验证奖励（outcome-only GRPO）会导致两个常见失败模式？

终点奖励能验证是否成功，但缺少对推理路径的反馈：在成功前，接近成功的前置进展难以获得有效信号；在成功后，终点奖励无法区分组织良好的正确推理与冗余或局部有误的推理过程。

SCOPE-RL如何在“成功之前”和“成功之后”分别 densify 奖励信号？

SCOPE-RL采用两阶段：Adaptive Scaffolded RL在成功前对答案隐藏的子问题链使用前缀分解的可验证奖励，以奖励部分进展；Quality-Aware Process RL在成功后对达到正确答案的轨迹应用正确性门控的过程形状奖励，偏好有用且结构良好的推理。

Step-Quality Evaluation Protocol如何评估超出最终答案准确率的推理质量？

该协议在保留规则化答案验证的同时，引入基于后验LLM判断的诊断，用于衡量有用步骤密度、低价值步骤比例、错误定位与令牌效率。

Jul 16, 2026

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