# OneShotTrainingExample: A One-Shot RLVR Selector Training Framework for Mathematical Reasoning Models

> A unified workspace integrating GHPO/Open-R1 training code and one-shot RLVR selector experiments, providing a complete training, evaluation, and analysis workflow for improving mathematical reasoning models.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-13T19:39:24.000Z
- 最近活动: 2026-05-13T19:47:11.600Z
- 热度: 148.9
- 关键词: 强化学习, 数学推理, RLVR, GHPO, 大语言模型, 训练框架, 选择器机制
- 页面链接: https://www.zingnex.cn/en/forum/thread/oneshottrainingexample-rlvr
- Canonical: https://www.zingnex.cn/forum/thread/oneshottrainingexample-rlvr
- Markdown 来源: floors_fallback

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## Introduction: Core Overview of the OneShotTrainingExample Project

OneShotTrainingExample is a unified workspace that integrates GHPO/Open-R1 training code and one-shot RLVR selector experiments. It aims to efficiently enhance the performance of mathematical reasoning models through the one-shot RLVR selector, providing a complete training, evaluation, and analysis workflow. This addresses the issues of insufficient deep reasoning capabilities in traditional supervised fine-tuning (SFT) and the high resource consumption and complex tuning of reinforcement learning (RL) training.

## Project Background and Core Objectives

### Project Background
In the field of large language models, mathematical reasoning ability is a key indicator of model intelligence. Traditional supervised fine-tuning (SFT) lacks deep reasoning capabilities for complex mathematical problems; while reinforcement learning (RL) can guide models to learn reasoning strategies, it requires significant computational resources and complex hyperparameter tuning.

### Core Objectives
The OneShotTrainingExample project provides a unified workspace, integrating GHPO and Open-R1 training code, focusing on efficiently improving the performance of mathematical reasoning models through the one-shot RLVR selector.

## Core Methods: GHPO and One-Shot RLVR Selector

### GHPO: Group Hindsight Policy Optimization
- Definition: Group Hindsight Policy Optimization, compared to traditional PPO, uses group sampling results to guide policy updates
- Advantages: More efficient use of computational resources, extracting training signals from multiple candidate answers (even if partially correct)
- Code Support: Includes complete training code, configuration files, and scripts, supporting fine-tuning of models like Qwen2.5-Math-7B, with hyperparameters managed via YAML

### One-Shot RLVR Selector Mechanism
- Definition: A reinforcement learning paradigm based on verifiable rewards, training a selector to judge whether a candidate reasoning path leads to the correct answer
- Advantages: Reduces training difficulty (only evaluation instead of generation), improves sample efficiency (utilizes existing paths), and enhances interpretability (analyzes decision-making processes)
- Experiment Support: Provides a complete Jupyter Notebook experimental workflow, forming a closed loop from reasoning to selector experiments

## Experimental Workflow and Phase Division

The project's experiments follow a step-by-step principle, divided into multiple phases:
1. **Phase 0**: Establish basic reasoning capabilities, collect baseline data using pre-trained models
2. **Phase 1**: Improve reasoning tests, explore prompt strategies and reasoning path generation methods
3. **Phase 4**: Core selector experiments, train multi-variant selectors and compare performance on the validation set
4. **Phase 5**: Build a complete training loop, combining the selector with the base model to achieve end-to-end improvement

Each phase has a corresponding Notebook that records steps, parameters, and result visualization, facilitating reproduction and adjustment.

## Research Findings and Documentation Resources

- **Research_Findings.md**: Summarizes key experimental findings, including quantitative results (accuracy improvement) and qualitative analysis (performance differences across problems of varying difficulty)
- **presentation_script.md**: Presentation script, suitable for academic conferences or team sharing
- **RL-FinalPush.ipynb**: Summary Notebook, integrating results from all phases, providing performance comparisons and conclusions

Documentation ensures research results are traceable and reproducible.

## Tech Stack and Deployment Recommendations

### Tech Stack
- Training framework: PyTorch
- Model operations: Transformers library
- Experimental environment: Jupyter
- Dependency management: requirements.txt lists all Python packages

### Deployment Recommendations
1. Requires GPU resources (especially for 7B parameter models)
2. Large model weights/checkpoints are not committed to Git
3. Execute Notebooks in the order specified in Documents/Execution-Step_Smit.txt

## Application Scenarios and Future Outlook

### Application Scenarios
- Math education: Evaluate the quality of students' problem-solving processes
- Model development: Provide an efficient RL training path
- Academic research: Open up a new direction of validator design guiding generative models

### Future Outlook
- Expand to code generation, logic puzzles, scientific question answering, and other fields
- Community contributions: Support more base models, optimize selector architecture, and apply to new tasks

The open-source nature of the project provides space for community improvements.