BPM

BPM · GLM-Z1-9B → Qwen3.5-2B

arXiv:2607.22334 · Project page

Qwen3.5-2B distilled from GLM-Z1-9B-0414 with BPM (Byte-Prefix Marginalization), a cross-tokenizer on-policy distillation method. Forward-KL arm, step 149.

Results

Every cell is avg@8 / pass@8, matching Table 1 of the paper. Sampling: temperature 0.6, top-p 0.95, top-k 20, 8 samples per prompt, thinking enabled.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok   = AutoTokenizer.from_pretrained("K1zE/BPM")
model = AutoModelForCausalLM.from_pretrained("K1zE/BPM", dtype="auto", device_map="auto")

msgs = [{"role": "user", "content": "What is the remainder of 7^100 modulo 13?"}]
ids  = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=2048)[0][ids.shape[-1]:], skip_special_tokens=True))

Prompts: K1zE/BPM. Research checkpoint, not instruction-tuned for general use.

Citation

@misc{wang2026crosstokenizeronpolicydistillationbyteprefix,
      title={Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization}, 
      author={Hao Wang and Kun Yuan and Wenlin Zhong and Minglei Zhang and Han Xiao and Ming Sun and Honggang Qi},
      year={2026},
      eprint={2607.22334},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2607.22334}, 
}