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title: "Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models — Stuff That Spins"
description: "arXiv:2607.02869v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathema…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:05:05.543138+00:00"
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# Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.02869  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.02869v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathematical reasoning in language models. Yet most RLVR work rewards only the final answer (outcome-based rewards), leaving the impact of step-level process supervision (process rewards) underexplored especially for small models that lack the capacity to self-correct under sparse feedback. We systematically compare five reward conditions applied

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