---
id: 20260701-T0-12
title: "Tandem RL：让弱模型也能通过可验证奖励掌握推理能力"
title_en: "Tandem RL: Enabling Weaker Agents to Master Reasoning via Verifiable Rewards"
url: https://ai.daily.yangsir.net/daily/20260701-T0-12
issue_date: 2026-07-01
publish_date: 2026-06-30T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2606.28166
---

# Tandem RL：让弱模型也能通过可验证奖励掌握推理能力

新论文探讨了Tandem Reinforcement Learning（串联强化学习）。研究证明，使用可验证奖励（如数学证明）不仅能提升强模型能力，还能让弱模型通过学习强模型的数据，达到专家级甚至超人类的推理表现。

## English Version

**Tandem RL: Enabling Weaker Agents to Master Reasoning via Verifiable Rewards**

This research explores Tandem RL with verifiable rewards. It demonstrates that weaker agents can achieve expert or superhuman reasoning performance by learning from stronger agents using verifiable domains like competition math.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2606.28166)

**详情页**：https://ai.daily.yangsir.net/daily/20260701-T0-12

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