# hkust-nlp/simplerl-reason

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3,867 stars · 286 forks · Python · MIT

## Links

- GitHub: https://github.com/hkust-nlp/simpleRL-reason
- awesome-repositories: https://awesome-repositories.com/repository/hkust-nlp-simplerl-reason.md

## Description

simpleRL-reason is a training framework designed to improve mathematical and logical deduction in large language models. It utilizes reinforcement learning and policy optimization to enhance the accuracy and transparency of step-by-step deduction chains.

The project implements a pipeline that establishes baseline capabilities through supervised fine-tuning before applying reinforcement learning to maximize deductive accuracy. It features a reward modeling toolkit that calculates scalar feedback by comparing generated reasoning steps against verified mathematical ground truths.

The framework includes a standardized benchmark for measuring reasoning performance and a web-based interface for visualizing the evolution of logical processes. It further incorporates curated difficulty-based datasets and a dependency-free execution suite to analyze model responses.

## Tags

### Artificial Intelligence & ML

- [Mathematical Reasoning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/large-scale-training/mathematical-reasoning-training.md) — Implements a comprehensive training methodology for improving model performance on complex mathematical problem-solving tasks.
- [Rule-Based Reward Models](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/reward-modeling/rule-based-reward-models.md) — Calculates scalar feedback by comparing generated reasoning steps against verified mathematical ground truths.
- [Reasoning Training Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/large-language-model-training-frameworks/reasoning-training-frameworks.md) — Provides a framework for improving mathematical and logical deduction in LLMs using reinforcement learning and policy optimization.
- [Supervised Fine-Tuning Workflows](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/training-frameworks/large-language-model-training-frameworks/rl-post-training/supervised-fine-tuning-workflows.md) — Implements supervised fine-tuning workflows to establish baseline logical capabilities before applying reinforcement learning.
- [Policy Gradient Optimizers](https://awesome-repositories.com/f/artificial-intelligence-ml/policy-gradient-optimizers.md) — Utilizes gradient-based methods to optimize model policy parameters for improved logical accuracy in deduction.
- [Reinforcement Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning.md) — Applies reinforcement learning techniques to refine model outputs based on verified ground truth answers and scalar feedback.
- [Reasoning Chain Training](https://awesome-repositories.com/f/artificial-intelligence-ml/reinforcement-learning-training-utilities/reasoning-chain-training.md) — Uses reinforcement learning with rule-based rewards to enable models to learn extended reasoning chains spontaneously. ([source](https://cdn.jsdelivr.net/gh/hkust-nlp/simplerl-reason@v1/README.md))
- [Deduction Chains](https://awesome-repositories.com/f/artificial-intelligence-ml/step-by-step-task-plans/deduction-chains.md) — Structures model outputs as sequential logical deduction chains to improve the transparency and accuracy of mathematical reasoning.
- [Reasoning Process Monitors](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/ai-observability-evaluation/reasoning-process-monitors.md) — Provides a web-based interface to visualize and audit the evolution of step-by-step reasoning chains across training stages. ([source](https://cdn.jsdelivr.net/gh/hkust-nlp/simplerl-reason@v1/README.md))
- [Difficulty-Based Datasets](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-management/evaluation-datasets/dataset-curation/difficulty-based-datasets.md) — Provides curated training and evaluation datasets organized by complexity to facilitate multi-step deductive problem solving.
- [Reasoning Evaluations](https://awesome-repositories.com/f/artificial-intelligence-ml/long-context-training-optimizations/long-context-retrieval-testing/reasoning-evaluations.md) — Provides a testing framework to measure the model's ability to reason and comprehend complex mathematical tasks.
- [Reasoning Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/reasoning-models/reasoning-optimization.md) — Enhances complex reasoning performance through two-stage reinforcement learning and value estimation.

### Scientific & Mathematical Computing

- [Mathematical Reasoning Evaluations](https://awesome-repositories.com/f/scientific-mathematical-computing/mathematical-problem-solving-toolkits/mathematical-reasoning-evaluations.md) — Includes a standardized evaluation suite to objectively measure numerical computation and multi-step problem-solving skills.

### Part of an Awesome List

- [Reinforcement Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/reinforcement-learning-frameworks.md) — Simplified reinforcement learning pipeline for reasoning tasks.