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PursuitOfDataScience
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Model Details
Model Provider
PursuitOfDataScience
Model Tree
Qwen/Qwen1.5-0.5B
Input Modalities
Output Modalities
Supported Functionality
Evaluated on contamination-free elementary math-word-problem benchmarks — SVAMP, ASDiv, MAWPS (clean)
and GSM-Plus (semi-clean, adversarial) — that appear in none of the training stages. n=500/set,
with-think decoding. Metrics: greedy / +budget-forcing / self-consistency@32 / pass@32 (%).
| set | greedy | +budget | self-cons@32 | pass@32 |
|---|---|---|---|---|
| SVAMP | 33.4 | 33.4 | 44.4 | 83.6 |
| ASDiv | 49.2 | 48.6 | 58.4 | 83.8 |
| MAWPS | 39.8 | 39.6 | 47.4 | 78.4 |
| GSM-Plus (adversarial) | 16.8 | 16.8 | 21.2 | 60.2 |
| mean (SVAMP/ASDiv/MAWPS) | 40.8 | 40.5 | 50.1 | 81.9 |
Read plainly: single-shot greedy is ~41% on the clean sets, self-consistency lifts it to ~50%, and the model can solve far more than it reliably answers in one pass — pass@32 ≈ 82%. These are absolute-modest numbers for a 0.46B model; it is a small reasoner, and the greedy→pass@K headroom is the interesting part.
Note on GSM8K: the CoT/STaR/GRPO training overlaps GSM8K, so GSM8K is not a valid held-out benchmark for this model. Use the clean SVAMP / ASDiv / MAWPS numbers above as the honest signal.
Base Qwen1.5-0.5B → five stages (base-agnostic harness reasoning/reason_control/ + RLVR in the
training repo):
<think>…</think> + \boxed{} math-reasoning mix (LR 1e-5, 1 epoch).\boxed on
GSM8K-train), early-stopped to avoid over-optimization.Cumulatively, greedy accuracy on the clean sets rose from ~24% (after stage 3) to ~41% (after stage 5) — STaR and RLVR mostly convert the large latent pass@K ceiling into single-shot answers rather than raising the ceiling itself.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchmid = "PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think"tok = AutoTokenizer.from_pretrained(mid)model = AutoModelForCausalLM.from_pretrained(mid, dtype=torch.bfloat16).to("cuda").eval()msgs = [{"role": "user", "content": "Ana has 3 boxes with 12 pencils each. She gives away 8. How many are left?"}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")out = model.generate(ids, max_new_tokens=512, do_sample=False)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))# -> <think> 3 * 12 = 36 ... 36 - 8 = 28 ... </think> The answer is $\boxed{28}$.
Uses the standard ChatML format (<|im_start|> / <|im_end|>). For the best accuracy, sample K≈16–32
traces (temperature 0.8, top_p 0.95) and majority-vote the \boxed{} answers (self-consistency ≈ 50%).
Fine-tuned from Qwen/Qwen1.5-0.5B (Apache-2.0) and released under Apache-2.0; please also observe the base model's terms.
Training + evaluation code: PursuitOfDataScience/ArgonneAI
(reasoning/reason_control/ recipe, reasoning/grpo.py RLVR, reasoning/clean_eval.py honest evaluator).
bibtex
@misc{argonne_qwen05b_think_2026,title = {Argonne-Qwen1.5-0.5B-think: a compact chain-of-thought reasoner (SFT->DPO->CoT->STaR->RLVR)},author = {PursuitOfDataScience},year = {2026},url = {https://huggingface.co/PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think}}