devshaheen/qwen3.5_0.6B_rlvr_grpo_checkpoints

Qwen3-0.6B RLVR-GRPO

Overview

This repository contains a Qwen3-0.6B checkpoint trained using Reinforcement Learning with Verifiable Rewards (RLVR) and Group Relative Policy Optimization (GRPO) on mathematical reasoning tasks.

The checkpoint was produced as part of the Open Post-Training System project, an open-source effort focused on reproducing and studying modern reasoning-model post-training techniques.

Open Post-Training System
https://github.com/shaheennabi/open-posttraining-system

Model Details

Property	Value
Base Model	Qwen/Qwen3-0.6B
Training Method	RLVR + GRPO
Domain	Mathematical Reasoning
Dataset	rasbt/math_full_minus_math500
Training Steps	Multiple GRPO Steps

Training Method

This model was trained using:

• Reinforcement Learning with Verifiable Rewards (RLVR)
• Group Relative Policy Optimization (GRPO)
• Multiple rollout generation per prompt
• Rule-based mathematical answer verification
• Outcome-based reward assignment

Rewards are assigned based on the correctness of the final answer rather than matching a reference reasoning trace.

Usage

This repository already includes:

• GRPO-trained checkpoint
• Tokenizer files
• Model configuration files

Load the model directly from Hugging Face using the Open Post-Training System loader:

from model_and_tokenizer import load_model_and_tokenizer

model, tokenizer = load_model_and_tokenizer(
    checkpoint_path="devshaheen/qwen3.5_0.6B_rlvr_grpo_checkpoints"
)

Example Prompt

Solve the following problem:

If a train travels 120 km in 2 hours, what is its average speed?

Intended Uses

• Mathematical reasoning experiments
• RLVR research
• GRPO research
• Reasoning model evaluation
• Open-source post-training research
• Educational purposes

Limitations

• Trained for only multiple steps GRPO optimization steps(multiple checkpoints there).
• Intended as an experimental research checkpoint.
• Additional RL training is expected to improve performance.
• Reasoning quality may vary across problem types.

Open Post-Training System

This model was trained using the Open Post-Training System:

https://github.com/shaheennabi/open-posttraining-system

The project provides open implementations of:

• Supervised Fine-Tuning (SFT)
• Reinforcement Learning with Verifiable Rewards (RLVR)
• Group Relative Policy Optimization (GRPO)
• Reasoning evaluation pipelines
• Inference-time scaling
• Open reasoning model development

Citation

If you use this model, please cite or acknowledge:

• Qwen3-0.6B
• math_full_minus_math500
• Open Post-Training System

Disclaimer

This repository contains an experimental research checkpoint and is not intended for production use.