XINLI1997

DN-MOPD-Qwen3.5-9B-teacher-if

README

License: apache-2.0

Model details

Table

Base model	Qwen/Qwen3.5-9B
Role	Instruction-following (IF) expert; frozen teacher of the 9B students
Training	GRPO from the base model, 400 updates, seed 42
Precision	bfloat16
Chat format	non-thinking (`enable_thinking=False`)
License	Apache-2.0 (same as the base model)

Training recipe

Algorithm: GRPO on instruction-following (IF) prompts with a verifiable reward; no KL or entropy term.
Batching: 128 prompts per rollout, 8 responses per prompt, 256 responses per optimizer step. Dynamic sampling drops prompt groups without reward variation (at most 8 generation batches per rollout).
Lengths: prompt ≤ 2,048 tokens, response ≤ 8,192 tokens, temperature 1.0.
Optimizer: Adam, learning rate 1e-6 (constant after 10 warm-up updates), betas (0.9, 0.98), weight decay 0.1, gradient clipping 1.0.
Length of training: 400 updates (runs were capped at 400), seed 42.

The full recipe, with the launch scripts for every row of the paper's tables, is in recipes/qwen3.5/ and docs/recipe.md.

Usage

This model was trained and evaluated with the non-thinking chat format. Pass enable_thinking=False to the chat template. Qwen3.5-9B's chat template enables thinking by default, so this argument is required. The evaluation settings in the paper were temperature 1.0 and top-p 1.0, with up to 16,384 new tokens (8,192 in the appendix).

vLLM (the paper used vLLM 0.18.0):

python

from vllm import LLM, SamplingParams llm = LLM(model="XINLI1997/DN-MOPD-Qwen3.5-9B-teacher-if", max_model_len=32768)params = SamplingParams(temperature=1.0, top_p=1.0, max_tokens=16384, seed=42)messages = [{"role": "user", "content": "Find the sum of all positive divisors of 36. Put the final answer in \\boxed{}."}]outputs = llm.chat(messages, params, chat_template_kwargs={"enable_thinking": False})print(outputs[0].outputs[0].text)

Transformers (Qwen3.5 needs transformers>=5; the paper's training environment used 5.12.1):

python

import torchfrom transformers import AutoModelForImageTextToText, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("XINLI1997/DN-MOPD-Qwen3.5-9B-teacher-if")model = AutoModelForImageTextToText.from_pretrained("XINLI1997/DN-MOPD-Qwen3.5-9B-teacher-if", dtype=torch.bfloat16, device_map="auto")messages = [{"role": "user", "content": "Write a Python function that returns the n-th Fibonacci number."}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)inputs = tokenizer(text, return_tensors="pt").to(model.device)output = model.generate(**inputs, max_new_tokens=4096, do_sample=True, temperature=1.0, top_p=1.0)print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Evaluation

Paper Table 1 (Qwen3.5-9B):

Table with columns: Model, AIME25, AIME26, LCB v5, LCB v6, IFEval, IFBench, Total
Model	AIME25	AIME26	LCB v5	LCB v6	IFEval	IFBench	Total
DN-MOPD-Qwen3.5-9B-teacher-if (IF expert)	54.6	60.5	54.0	51.5	86.5	42.1	58.2
Initial student (Qwen3.5-9B)	57.7	62.6	54.9

Scores (%) from the paper; training seed 42; 16,384-token evaluation cap; non-thinking chat template; temperature 1.0, top-p 1.0, generation seed 42. AIME25/AIME26: avg@64. LiveCodeBench v5/v6 (167/175 disjoint problems): avg@6. IFEval/IFBench: strict prompt accuracy, avg@16. Total: mean of the six task scores.

Files

Weights in Hugging Face format (Qwen3_5ForConditionalGeneration, bfloat16), exported from the FSDP training checkpoint.
The export omits the 15 multi-token-prediction tensors (mtp.*) of the base model. All other tensors have the base model's names and shapes. MTP-based speculative decoding is therefore not available with this checkpoint. Ordinary decoding is unaffected: the paper's evaluations used exactly these files.
config.json, the tokenizer files and chat_template.jinja are the base model's, unchanged.
The vision encoder is carried over from the base model. Training and evaluation used text only.
LICENSE is the base model's Apache-2.0 license.

Limitations

A specialist: it was trained for one domain only. In the table above it scores below the initial model on AIME25, AIME26, LCB v5.
Trained with responses of at most 8,192 tokens and evaluated only in non-thinking mode; thinking mode, multimodal inputs, other languages and safety behaviour were not evaluated beyond the base model.

Citation

bibtex

@article{li2026dnmopd,  title   = {Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation},  author  = {Li, Xin and Jiang, Hao and Gao, Xin and Wang, Annan and Xie, Yuchen and Guo, Jinghao and Qu, Xingwei and Zhang, Yichi and Yuen, Chau},  journal = {arXiv preprint arXiv:2609.35347},  year    = {2026},  url     = {https://arxiv.org/abs/2609.35347}}

This model is a fine-tuned derivative of Qwen/Qwen3.5-9B by the Qwen team, released under the Apache License 2.0.

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Model Details

Model Provider

XINLI1997

Model Tree

Base

Qwen/Qwen3.5-9B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

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README

License: apache-2.0

Model details

Table

Base model	Qwen/Qwen3.5-9B
Role	Instruction-following (IF) expert; frozen teacher of the 9B students
Training	GRPO from the base model, 400 updates, seed 42
Precision	bfloat16
Chat format	non-thinking (`enable_thinking=False`)
License	Apache-2.0 (same as the base model)

Training recipe

Algorithm: GRPO on instruction-following (IF) prompts with a verifiable reward; no KL or entropy term.
Batching: 128 prompts per rollout, 8 responses per prompt, 256 responses per optimizer step. Dynamic sampling drops prompt groups without reward variation (at most 8 generation batches per rollout).
Lengths: prompt ≤ 2,048 tokens, response ≤ 8,192 tokens, temperature 1.0.
Optimizer: Adam, learning rate 1e-6 (constant after 10 warm-up updates), betas (0.9, 0.98), weight decay 0.1, gradient clipping 1.0.
Length of training: 400 updates (runs were capped at 400), seed 42.

The full recipe, with the launch scripts for every row of the paper's tables, is in recipes/qwen3.5/ and docs/recipe.md.

Usage

vLLM (the paper used vLLM 0.18.0):

python

from vllm import LLM, SamplingParams llm = LLM(model="XINLI1997/DN-MOPD-Qwen3.5-9B-teacher-if", max_model_len=32768)params = SamplingParams(temperature=1.0, top_p=1.0, max_tokens=16384, seed=42)messages = [{"role": "user", "content": "Find the sum of all positive divisors of 36. Put the final answer in \\boxed{}."}]outputs = llm.chat(messages, params, chat_template_kwargs={"enable_thinking": False})print(outputs[0].outputs[0].text)

Transformers (Qwen3.5 needs transformers>=5; the paper's training environment used 5.12.1):

python

import torchfrom transformers import AutoModelForImageTextToText, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("XINLI1997/DN-MOPD-Qwen3.5-9B-teacher-if")model = AutoModelForImageTextToText.from_pretrained("XINLI1997/DN-MOPD-Qwen3.5-9B-teacher-if", dtype=torch.bfloat16, device_map="auto")messages = [{"role": "user", "content": "Write a Python function that returns the n-th Fibonacci number."}]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)inputs = tokenizer(text, return_tensors="pt").to(model.device)output = model.generate(**inputs, max_new_tokens=4096, do_sample=True, temperature=1.0, top_p=1.0)print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Evaluation

Paper Table 1 (Qwen3.5-9B):

Table with columns: Model, AIME25, AIME26, LCB v5, LCB v6, IFEval, IFBench, Total
Model	AIME25	AIME26	LCB v5	LCB v6	IFEval	IFBench	Total
DN-MOPD-Qwen3.5-9B-teacher-if (IF expert)	54.6	60.5	54.0	51.5	86.5	42.1	58.2
Initial student (Qwen3.5-9B)	57.7	62.6	54.9

Files

Weights in Hugging Face format (Qwen3_5ForConditionalGeneration, bfloat16), exported from the FSDP training checkpoint.
The export omits the 15 multi-token-prediction tensors (mtp.*) of the base model. All other tensors have the base model's names and shapes. MTP-based speculative decoding is therefore not available with this checkpoint. Ordinary decoding is unaffected: the paper's evaluations used exactly these files.
config.json, the tokenizer files and chat_template.jinja are the base model's, unchanged.
The vision encoder is carried over from the base model. Training and evaluation used text only.
LICENSE is the base model's Apache-2.0 license.

Limitations

A specialist: it was trained for one domain only. In the table above it scores below the initial model on AIME25, AIME26, LCB v5.
Trained with responses of at most 8,192 tokens and evaluated only in non-thinking mode; thinking mode, multimodal inputs, other languages and safety behaviour were not evaluated beyond the base model.

Citation

bibtex

@article{li2026dnmopd,  title   = {Beyond Teacher Assignment: Domain-Normalized Multi-Teacher On-Policy Distillation},  author  = {Li, Xin and Jiang, Hao and Gao, Xin and Wang, Annan and Xie, Yuchen and Guo, Jinghao and Qu, Xingwei and Zhang, Yichi and Yuen, Chau},  journal = {arXiv preprint arXiv:2609.35347},  year    = {2026},  url     = {https://arxiv.org/abs/2609.35347}}

This model is a fine-tuned derivative of Qwen/Qwen3.5-9B by the Qwen team, released under the Apache License 2.0.