Argonne-Qwen1.5-0.5B-think

A 0.46B-parameter chain-of-thought reasoner: the base model Qwen1.5-0.5B fine-tuned with a fully open five-stage reasoning recipe — general SFT → DPO → CoT-SFT → STaR self-improvement → GRPO/RLVR (verifiable reward). It produces an explicit <think> … </think> reasoning trace followed by a final \boxed{} answer on math word problems.

The point of this model is to show how much math-reasoning ability a well-pretrained sub-1B base can be given by a light, fully open post-training recipe. It is a compact research reasoner — useful for studying test-time compute on small models — not a production-grade or competition-math solver.

Benchmarks (honest, held-out)

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.

How it was trained

Base Qwen1.5-0.5B → five stages (base-agnostic harness reasoning/reason_control/ + RLVR in the training repo):

1. General SFT on HuggingFaceH4/ultrachat_200k (LR 2e-5).
2. DPO preference alignment on argilla/dpo-mix-7k (LR 5e-6, β 0.1).
3. CoT-SFT on a <think>…</think> + \boxed{} math-reasoning mix (LR 1e-5, 1 epoch).
4. STaR — self-distillation on the model's own verified-correct, concise (terminating) traces on GSM8K-train + MATH-L1-3 (two rounds; fixes the non-termination that caps greedy).
5. GRPO / RLVR — group-relative policy optimization with a verifiable reward (correct \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.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

mid = "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%).

Limitations

• 0.46B parameters — a small reasoner. Absolute accuracy on clean grade-school math is ~41% greedy / ~50% self-consistency; expect errors, especially on multi-step or adversarial problems (GSM-Plus greedy ~17%).
• Scope: trained and evaluated on English elementary math word problems + general chat. Not a general-purpose or competition-math model.
• GSM8K is not a valid benchmark here (train-mix overlap) — use the clean numbers above.

Base model & license

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.

Provenance

Training + evaluation code: PursuitOfDataScience/ArgonneAI (reasoning/reason_control/ recipe, reasoning/grpo.py RLVR, reasoning/clean_eval.py honest evaluator).

Citation

@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}
}