# awesome-RLVR-boundary: Resource Collection for Reinforcement Learning with Verifiable Rewards (RLVR) and LLM Reasoning Boundaries

> This project compiles selected resources on Reinforcement Learning with Verifiable Rewards (RLVR) and the reasoning capability boundaries of large language models (LLMs), providing researchers with a systematic learning reference.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-03-27T04:42:51.000Z
- 最近活动: 2026-03-27T04:50:18.957Z
- 热度: 146.9
- 关键词: RLVR, 强化学习, 大语言模型, 推理能力, 资源汇总, AI安全
- 页面链接: https://www.zingnex.cn/en/forum/thread/awesome-rlvr-boundary-llm
- Canonical: https://www.zingnex.cn/forum/thread/awesome-rlvr-boundary-llm
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: awesome-RLVR-boundary: Resource Collection for Reinforcement Learning with Verifiable Rewards (RLVR) and LLM Reasoning Boundaries

This project compiles selected resources on Reinforcement Learning with Verifiable Rewards (RLVR) and the reasoning capability boundaries of large language models (LLMs), providing researchers with a systematic learning reference.

## Project Introduction

**awesome-RLVR-boundary** is a carefully curated resource collection focusing on two cutting-edge research directions:

1. **Reinforcement Learning with Verifiable Rewards (RLVR)**
2. **LLM Reasoning Capability Boundaries**

## What is RLVR?

RLVR (Reinforcement Learning with Verifiable Rewards) is a reinforcement learning paradigm where reward signals are verifiable, rather than relying on human preferences or subjective judgments. This is particularly important in tasks such as mathematical reasoning and code generation.

## Why Focus on Reasoning Boundaries?

With the emergence of reasoning models like DeepSeek-R1 and OpenAI o1, understanding the reasoning capability boundaries of LLMs has become crucial:
- Which tasks can be reliably solved?
- Where are the model's limitations?
- How to further improve reasoning capabilities?

## Resource Value

This project provides researchers with:
- Systematic literature compilation
- Links to key papers and code
- An overview of the field's development path

## Target Audience

- Reinforcement learning researchers
- LLM reasoning capability researchers
- AI alignment and safety researchers

## Resource Links

- GitHub Repository: https://github.com/rorofaiz/awesome-RLVR-boundary