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Siyuc
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Model Details
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Siyuc
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Base
Qwen/Qwen3-4B-Base
Fine-tuned
this model
Input Modalities
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Output Modalities
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Supported Functionality
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Container
Qwen/Qwen3-4B-BaseINFUSERSiyuc/infuser-data| Category | Benchmark | Score |
|---|---|---|
| General | MMLU-Pro | 60.68% |
| General | GPQA-Diamond | 35.35% |
| General | SuperGPQA | 33.90% |
| General | BBEH | 12.57% |
| Math & physics | MATH500 | 77.90% |
| Math & physics | AIME2024 |
Category and overall means are computed over the same benchmark groups as the paper's main table. INFUSER avg is the average over three seeded INFUSER runs. R-Few (paper) and SPICE (paper) are self-reported values from their original papers, so missing categories are shown as -.
| Category | This model | INFUSER avg | Base | R-Zero | AZR | R-Few (paper) | SPICE (paper) |
|---|---|---|---|---|---|---|---|
| General reasoning | 35.62% | 35.43% | 29.46% | 32.18% | 32.93% | 34.33% | 35.00% |
| Math & physics reasoning | 25.93% | 25.73% | 21.34% |
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo_id = "Siyuc/INFUSER-Qwen3-4B-base"tokenizer = AutoTokenizer.from_pretrained(repo_id)model = AutoModelForCausalLM.from_pretrained(repo_id)
bibtex
@misc{chen2026infuser,title = {INFUSER: Influence-Guided Self-Evolution Improves Reasoning},author = {Siyu Chen and Miao Lu and Beining Wu and Heejune Sheen and Fengzhuo Zhang and Shuangning Li and Zhiyuan Li and Jose Blanchet and Tianhao Wang and Zhuoran Yang},year = {2026},eprint = {2606.09052},archivePrefix = {arXiv},primaryClass = {cs.LG},url = {https://arxiv.org/abs/2606.09052}}
| 11.87% |
| Math & physics | AIME2025 | 11.56% |
| Math & physics | HMMT | 3.19% |
| Math & physics | OlympiadBench (Math) | 42.14% |
| Math & physics | OlympiadBench (Phys) | 8.90% |
| Medical | MedQA | 59.47% |
| Medical | MedXpertQA | 13.80% |
| Coding | HumanEval+ | 75.23% |
| Coding | LiveCodeBench v1-5 | 23.01% |
| 25.12% |
| 26.51% |
| - |
| - |
| Medical | 36.63% | 36.32% | 34.24% | 36.75% | 36.14% | - | - |
| Coding | 49.12% | 48.63% | 45.47% | 47.65% | 47.49% | - | - |
| Overall (14 benchmarks) | 33.54% | 33.28% | 28.95% | 32.01% | 32.71% | - | - |