This is the Windows app named EasyR1 whose latest release can be downloaded as v0.3.2_RLBaselinessourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS
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EasyR1
DESCRIPTION
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. For teams exploring open reasoning models, EasyR1 provides an opinionated yet flexible path from dataset to deployable checkpoints.
Features
- One-command pipelines for SFT and reasoning-oriented optimization
- Support for popular open models and tokenizers with long-context settings
- Memory-efficient training via parameter-efficient finetuning and gradient tricks
- Pluggable data pipelines for reasoning traces, rationales, and verifier signals
- Built-in validation on reasoning-centric tasks (math, coding, stepwise QA)
- Clear experiment structure for comparing techniques and tracking runs
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/easyr1.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
