BioReason-Pro RL
Reinforcement learning (GRPO) optimized checkpoint of BioReason-Pro, a multimodal reasoning LLM for protein function prediction. This model builds on the SFT checkpoint and is further optimized through group relative policy optimization to improve reasoning quality and GO term prediction accuracy.
Training data: wanglab/bioreason-pro-rl-reasoning-data
See also:
- BioReason-Pro SFT - supervised fine-tuned checkpoint
- GO-GPT - autoregressive GO term predictor
Citation
If you find this work useful, please cite our papers:
@article {Fallahpour2026.03.19.712954,
author = {Fallahpour, Adibvafa and Seyed-Ahmadi, Arman and Idehpour, Parsa and Ibrahim, Omar and Gupta, Purav and Naimer, Jack and Zhu, Kevin and Shah, Arnav and Ma, Shihao and Adduri, Abhinav and G{\"u}loglu, Talu and Liu, Nuo and Cui, Haotian and Jain, Arihant and de Castro, Max and Fallahpour, Amirfaham and Cembellin-Prieto, Antonio and Stiles, John S. and Nem{\v c}ko, Filip and Nevue, Alexander A. and Moon, Hyungseok C. and Sosnick, Lucas and Markham, Olivia and Duan, Haonan and Lee, Michelle Y. Y. and Salvador, Andrea F. M. and Maddison, Chris J. and Thaiss, Christoph A. and Ricci-Tam, Chiara and Plosky, Brian S. and Burke, Dave P. and Hsu, Patrick D. and Goodarzi, Hani and Wang, Bo},
title = {BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning},
elocation-id = {2026.03.19.712954},
year = {2026},
doi = {10.64898/2026.03.19.712954},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954},
eprint = {https://www.biorxiv.org/content/early/2026/03/20/2026.03.19.712954.full.pdf},
journal = {bioRxiv}
}
@misc{fallahpour2025bioreasonincentivizingmultimodalbiological,
title={BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model},
author={Adibvafa Fallahpour and Andrew Magnuson and Purav Gupta and Shihao Ma and Jack Naimer and Arnav Shah and Haonan Duan and Omar Ibrahim and Hani Goodarzi and Chris J. Maddison and Bo Wang},
year={2025},
eprint={2505.23579},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.23579},
}