# GPT-OSS Reinforcement Learning: Revolutionizing AI Applications

GPT-OSS is a cutting-edge architecture from OpenAI that has the potential to power breakthrough AI applications. However, training these models with reinforcement learning (RL) was previously limited to well-funded labs with high-performance GPUs like H100s. Today, we are excited to announce that you can now train GPT-OSS with RL and Generalized Policy Gradient (GPRO) using Unsloth, a revolutionary framework that offers unparalleled performance gains.

## Unveiling Unsloth: The Revolutionary Framework

Unsloth is the brainchild of our team, designed to tackle the challenges of training GPT-OSS with RL. This innovative framework leverages advanced techniques such as weight sharing, Flex Attention, Standby, and custom kernels to achieve unprecedented speed, memory efficiency, and accuracy.

### Key Features of Unsloth

* **Fastest Inference**: Unsloth achieves 3x faster inference speeds compared to other implementations, making it an ideal choice for large-scale RL applications. * **Lowest VRAM Usage**: By utilizing 4-bit weights and reducing the number of VRAM allocations, Unsloth achieves 50% less VRAM usage than other frameworks, resulting in significant memory savings. * **Longest Context**: Unsloth's custom kernel implementation allows for 8x longer context, enabling more efficient exploration-exploitation trade-offs during RL training.

## Training GPT-OSS with Unsloth

To get started with training GPT-OSS using Unsloth, you can explore our free notebook: gpt-oss-20b GRPO Colab notebook. This interactive tutorial provides a comprehensive guide to setting up the framework, configuring Hyperparameters, and optimizing RL policies.

### Benefits of Using Unsloth

* **No Accuracy Degradation**: Despite achieving significant performance gains, Unsloth ensures that RL training accuracy remains unaffected. * **VLLM Compatibility**: As vLLM becomes compatible with RL, Unsloth will seamlessly integrate its 50% weight sharing feature to provide even greater efficiency. * **Flexibility and Customizability**: With the flexibility to adjust kernel optimizations, reward function modifications, and hyperparameters, you can fine-tune your training pipeline for optimal results.

## Counteracting Reward Hacking

One of the most significant challenges in RL is counteracting "reward hacking," where models exploit techniques to increase rewards without actually performing the desired task. Unsloth includes a free code generation notebook (available here) that explores solutions to common reward hacking issues, such as:

* **Code Generation**: By using Numpy or Torch libraries and caching results, you can prevent models from exploiting optimized CUDA kernels. * **Global Variables**: By restricting access to global variables and using types.FunctionType, you can prevent models from modifying Python global variables.

## Conclusion

GPT-OSS Reinforcement Learning with Unsloth is poised to revolutionize AI applications. With its unparalleled performance gains, flexibility, and customizability, this framework is ideal for large-scale RL projects. Don't miss the opportunity to harness the power of GPT-OSS with Unsloth today!

Join our community and explore the potential of GPT-OSS Reinforcement Learning with Unsloth.

### Free Resources

* Free GPT-oss-20b GRPO Colab notebook * Unsloth Framework Repository

### Get Started Today

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Get started with Unsloth today!

Join our community and explore the potential of GPT-OSS Reinforcement Learning with Unsloth.