---
license: mit
library_name: transformers
pipeline_tag: text-generation
base_model: UCL-CSSB/PlasmidGPT
tags:
- biology
- plasmid
- dna
- synthetic-biology
- gpt2
- grpo
- reinforcement-learning
---

# PlasmidGPT-GRPO

GRPO reinforcement-learning fine-tune of [PlasmidGPT](https://huggingface.co/UCL-CSSB/PlasmidGPT), trained against a multi-component biological reward (functional annotations, length prior, repeat penalty, cassette ordering). Camera-ready model for the ICML 2026 paper *Effects of Structural Reward Shaping on Biophysical Properties in RL-Trained Plasmid Generators*.

## Quick start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO")
tokenizer = AutoTokenizer.from_pretrained("UCL-CSSB/PlasmidGPT-GRPO")

input_ids = tokenizer("ATG", return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_new_tokens=512, do_sample=True, temperature=1.0)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Recommended sampling: T=1.0 for direct generation, T=1.15 for rejection sampling (per the paper).

## Citation

```bibtex
@inproceedings{thiel2026plasmidrl,
  title     = {Effects of Structural Reward Shaping on Biophysical Properties in {RL}-Trained Plasmid Generators},
  author    = {Thiel, McClain and Cunningham, Angus G. and Barnes, Chris P.},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  year      = {2026}
}
```