---
library_name: transformers
license: mit
---

# Model Card for Model ID

This is a saved checkpoint from fine-tuning a meta-llama/Meta-Llama-3.1-8B-Instruct model using supervised fine-tuning for our paper [**"Training a Generally Curious Agent"**](https://arxiv.org/abs/2502.17543). In our work, we introduce PAPRIKA, a finetuning framework for teaching large language models (LLMs) strategic exploration.

# NOTE:

In PAPRIKA, our training process consists of two stages. The first stage is supervised finetuning, and then we run preference finetuning using the RPO objective on top of the checkpoint obtained from supervised finetuning. 

We previously released the final checkpoint (after SFT followed by RPO finetuning). Due to [community request](https://github.com/tajwarfahim/paprika/issues/4), we are also releasing the checkpoint obtained after the first stage of SFT (i.e., the checkpoint before running RPO).

## Model Details

### Model Description

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This is the model card of a meta-llama/Meta-Llama-3.1-8B-Instruct model fine-tuned using PAPRIKA.

- **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct

### Model Sources

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- **Repository:** [Official Code Release for the paper "Training a Generally Curious Agent"](https://github.com/tajwarfahim/paprika)
- **Paper:** [Training a Generally Curious Agent](https://arxiv.org/abs/2502.17543)
- **Project Website:** [Project Website](https://paprika-llm.github.io)

## Training Details

### Training Data

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Our training dataset for supervised fine-tuning can be found here: [SFT dataset](https://huggingface.co/datasets/ftajwar/paprika_SFT_dataset)

Similarly, the training dataset for preference fine-tuning can be found here: [Preference learning dataset](https://huggingface.co/datasets/ftajwar/paprika_preference_dataset)

### Training Procedure

The [attached Wandb link](https://wandb.ai/llm_exploration/paprika_more_data?nw=nwusertajwar) shows the training loss per gradient step during both supervised fine-tuning and preference fine-tuning.


#### Training Hyperparameters

For supervised fine-tuning, we use the AdamW optimizer with learning rate 1e-6, batch size 32, cosine annealing learning rate decay with warmup ratio 0.04, and we train on a total of 17,181 trajectories.

For preference fine-tuning, we use the RPO objective, AdamW optimizer with learning rate 2e-7, batch size 32, cosine annealing learning rate decay with warmup ratio 0.04, and we train on a total of 5260 (preferred, dispreferred) trajectory pairs.

#### Hardware

This model has been finetuned using 8 NVIDIA L40S GPUs.


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@misc{tajwar2025traininggenerallycuriousagent,
      title={Training a Generally Curious Agent}, 
      author={Fahim Tajwar and Yiding Jiang and Abitha Thankaraj and Sumaita Sadia Rahman and J Zico Kolter and Jeff Schneider and Ruslan Salakhutdinov},
      year={2025},
      eprint={2502.17543},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2502.17543}, 
}
```

## Model Card Contact

[Fahim Tajwar](mailto:tajwarfahim932@gmail.com)