Argonne-3.5-think

Argonne 3.5-think

Argonne 3.5-think is a 2.88B-parameter reasoning model trained from scratch, built on argonne-3.5-base. It emits an explicit <think>…</think> trace and then a \boxed{} answer.

It is the successor to Argonne-3.0-think.

What changed in this revision (2026-08-04)

The previous release was trained on a corrupted view of its own data, and this one is not. Two argparse defaults in reasoning/cot-sft.py — --max_think_tokens 128 and --preserve_raw_reasoning 0 — silently truncated reasoning traces mid-derivation and dropped rows. Between them they removed about a third of the chain-of-thought tokens, discarded 80.7% of the arithmetic drill tier, and cut the concluding sentence from most targets. No launcher passed these flags, so every earlier run inherited them.

Fixing the two defaults — no new data, no new method, same recipe — produced this model. The most consequential effect is on single-step arithmetic, which the previous release got wrong roughly half the time:

The previous card carried this limitation: "Think-mode can over-step trivial arithmetic. On 'What is 17 − 5?' … the think trace has been observed computing 17−5=12 and then subtracting 5 again to answer 7." That was the truncated-data defect showing through, and it is fixed here.

Replicated at three independent seeds before release: the five-set mean is 57.25 / 57.35 / 57.38 (spread 0.13pt) and arithmetic is 142/144, 143/144, 144/144.

Evaluation

Greedy, paired against the previous release on identical items. n = 1000 (ASDiv, SVAMP), 500 (MAWPS, GSM-Plus), 319 (MATH-500). Significance is exact McNemar on the paired outcomes.

With test-time sampling (K=8, temperature 0.8):

GSM8K is contaminated for Argonne reasoning models and is deliberately not reported. GSM-Plus is adversarially perturbed GSM8K test, so it was audited directly: the training mix's GSM8K tier is 4,338/4,338 from the train split with zero test items, and no judged GSM-Plus item exceeds Jaccard 0.60 against any training row (0 hits at ≥0.70 across all 9,233 pool items). That +14.00 is not memorisation leaking through the perturbation.

MATH-500 carries measured indirect leakage and should be read with that in mind. 17 of its 319 items have a near-duplicate in the training mix (worst pair identical except for one digit), inherited from OpenMathReasoning/Mixture-of-Thoughts-derived tiers. Re-scored on the 302 clean items this model gets 39.07 versus 39.18 on the full pool, and the previous release 31.46 versus 31.66 — so the gap is unchanged and the leak does not inflate the comparison. The other four pools are clean by the same measure.

General capability

Flat. The arithmetic and word-problem gains did not come out of general ability.

Termination

The defining failure of the 3.0 line was non-termination — 50–60% of traces never closed </think>, so the answer was often never emitted. That was fixed by the short-trace mix and remains fixed here; budget-forcing adds ~1 point, which is the expected signature when there are no unclosed traces left to recruit.

Training

Relative to the previous release, stage 3 differs in exactly two ways: reasoning traces are preserved whole rather than cut at 128 tokens, and 2,000 rows of general-instruction anchor were added back. That second part matters — restoring the traces alone costs instruction-following (13/14 → 10/14); with the anchor restored it holds at 13/14 at every seed.

α = 0.85 is a real knee, not a default: α = 0.70 measurably reintroduces non-termination.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "PursuitOfDataScience/Argonne-3.5-think"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, dtype=torch.bfloat16
).cuda()

messages = [{"role": "user", "content": "A shop sells pencils 3 for $2. How much do 12 pencils cost?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
ids = tokenizer(text, return_tensors="pt")["input_ids"].cuda()

out = model.generate(ids, max_length=ids.shape[1] + 512, do_sample=False)
print(tokenizer.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

For throughput, prefer vLLM/SGLang over .generate().

Self-consistency is worth the extra samples. Sampling K=8 at temperature 0.8 and taking the majority answer moves ASDiv 74.90 → 81.20 and SVAMP 69.60 → 81.40.

Usage notes

• Load with trust_remote_code=True; config.json carries an auto_map so the custom argonne2 classes resolve without manual setup.
• The custom generate takes max_length (total length), not max_new_tokens.
• eos_token_id is 151645 (<|im_end|>) so the assistant turn ends cleanly. Verified for this revision: a chat-templated prompt with no eos_token_id argument terminates on its own.
• lm_head.weight is reported missing on load. Expected and benign — embeddings are tied.
• Context length 13,568, inherited from the base.

Limitations

• Verbose, and occasionally pads a correct answer with a wrong embellishment (e.g. appending "one of the four main stars in our solar system" to a correct statement that the sun is a star).
• pass@K is a noisy metric here. Re-running an identical model and seed reproduced greedy and self-consistency exactly but moved pass@8 by several points. Treat pass@K as a ceiling indicator; select on self-consistency or greedy.
• The instruction-following probe is 14 items. 13/14 at three seeds shows the regression from the data fix was repaired; it is not a broad instruction-following benchmark.
• MATH-500 is not a clean pool for this line — see the leakage measurement above. Quote the 302-item clean subset alongside it.
• Grade-school and early-competition arithmetic word problems are the measured domain. Code, tool-calling and general-purpose chat are not characterized for this revision.
• 2.88B parameters trained on 88.84B tokens — far below frontier compute.
• No safety alignment beyond what UltraChat and the preference data provide.

Source code

Everything below is on the GitHub main branch — PursuitOfDataScience/ArgonneAI.

Base model: argonne-3.5-base (training details).

Citation

@misc{argonne35think,
  author = {PursuitOfDataScience},
  title  = {Argonne 3.5-think},
  year   = {2026},
  publisher = {Hugging Face},
  url    = {https://huggingface.co/PursuitOfDataScience/Argonne-3.5-think}
}