Qwen2.5-Math 7B supervised-fine-tuned with the variational optimizer IVON, from the paper "Parameter Exploration for RLVR via Variational Learning".

This is a warm-start checkpoint: SFT'ing with IVON yields not just point weights but an approximate Gaussian posterior over them (a mean and a diagonal Hessian/precision estimate). That posterior is the learned prior used to seed the 3PO RLVR runs (Qwen-B3PO-7B / Qwen-M3PO-7B / Qwen-C3PO-7B), where weight perturbations sampled from it drive parameter-space exploration.

Training


Foundation model	`Qwen/Qwen2.5-Math-7B`
Stage	Warm-start SFT
Optimizer	IVON (see the paper for full hyperparameters)
Hardware	8× NVIDIA H200 (144 GB)

Usage

Loads as a standard causal LM:

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("BayesRL/Qwen2.5Math-IVON-SFT-7B")
tok = AutoTokenizer.from_pretrained("BayesRL/Qwen2.5Math-IVON-SFT-7B")

To use it as the warm-start prior for 3PO RLVR, load the IVON optimizer state via IVON_INIT_METHOD=trained in the companion code's run_rl.sh.

Citation

bibtex

@misc{venkatkrishna2026parameterexploration,
      title={Parameter Exploration for RLVR via Variational Learning}, 
      author={Vatsal Venkatkrishna and Nico Daheim and Iryna Gurevych},
      year={2026},
      eprint={2608.09805},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2608.09805}, 
}