---
title: "SCOPE-RL: Optimizing Reasoning Paths Before and After Success"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.11506v1"
permalink: "https://feed.astralor.com/items/itm_18835a97f5357ad9f880ccc4"
published: "2026-07-13T12:56:28.000Z"
score: 77
channel: "tech"
tags: ["GRPO","Qwen3","SCOPE-RL","Reward Densification","Reinforcement Learning","Reasoning Path Optimization"]
---

# SCOPE-RL: Optimizing Reasoning Paths Before and After Success

## AI commentary

> This is a highly practical paper, especially for teams training LLMs with RLVR. It cleverly addresses final reward sparsity by densifying process rewards, with experimental evidence of both accuracy and token efficiency improvements. If you are working on reasoning models or math reasoning tasks, this is worth reading.

## At a glance

Reinforcement Learning with Verifiable Rewards (RLVR) relies solely on sparse final-answer rewards, lacking direct feedback on reasoning paths. This paper introduces SCOPE-RL, a two-stage framework that densifies rewards before success via adaptive scaffolded RL and refines correct trajectories after success via quality-aware process rewards. On Qwen3-8B-Instruct, it improves accuracy by up to 11.2 percentage points and reduces reasoning tokens by up to 27.1%, with generalization to other settings.

## Key points

- Proposes SCOPE-RL, a two-stage framework to densify rewards before success and refine after success.
- Achieves up to 11.2 pp accuracy improvement and 27.1% token reduction on Qwen3-8B-Instruct and 0.6B-Instruct.
- Introduces an expert-validated Step-Quality Evaluation Protocol for useful-step density, error localization, and token efficiency.
- Code and data are open-sourced.

## Full text

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 two-stage 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 process-shape 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 Qwen3-8B-Instruct 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://github.com/tokencraft-lab/SCOPE-RL.

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Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_18835a97f5357ad9f880ccc4