An open olympiad-level competitive-programming reasoning model, fine-tuned from Qwen/Qwen3-32B. It's built to reason step by step through algorithmic programming problems and produce working code.

I trained this independently, as a solo project. It's a research artifact and an open contribution, not a product. If you build reasoning systems for code, it may be a useful starting point or comparison.

What it does

Given a competitive-programming problem (the kind where you read input, reason about an algorithm, and print output), the model works through the reasoning and writes a full program. It was trained specifically on that skill.

How it was trained

Two stages on top of the base model:

Supervised fine-tuning (SFT) on open code-reasoning datasets — RStar-Coder and OpenCodeReasoning — to teach step-by-step algorithmic reasoning and clean code output.
Reinforcement learning (GRPO) with a verifiable reward: the model generates candidate solutions, each is run against hidden test cases, and the fraction of tests it passes is the reward. No reward model or human labels — the signal is whether the code actually works.

SFT was run on 8×NVIDIA B200 GPUs, over roughly 1.8 billion tokens. The RL stage uses the same test-case-pass signal that makes the reward objective rather than a guess.

Intended use

Research on reasoning and RL-for-code.
A base to build on or fine-tune further.
Generating and studying solutions to algorithmic problems.

Limitations (read this)

It is not a frontier model. Large proprietary models (GPT, Claude, Gemini) are stronger at general coding. This is a specialized, independently-trained 32B, and you should expect it to be weaker outside its niche.
It can be confident and wrong. Always run and verify the code it produces.
It's tuned for competitive-programming-style problems; it is not a general assistant and has no additional safety tuning beyond the base model's.
No performance/benchmark claims are made here by choice — treat it as a research artifact and evaluate it yourself for your use case.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

name = "your-username/qwen3-32b-olympiad-cp"  # set to the published repo id
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, torch_dtype="auto", device_map="auto")

msgs = [{"role": "user", "content": "Read N then N integers and print their sum.\n\nWrite a complete program."}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=2048)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

License

Inherits the base model's license (Apache 2.0, from Qwen3-32B). Verify the base model's current license terms before commercial use.

Related work

cleanllm — my streaming JSONL cleaner for fine-tuning datasets (PyPI).
A verifiable competitive-programming RL environment (judges code against hidden tests).

Citation

If you use this model, a link back is appreciated:

@misc{qwen3_olympiad_cp,
  title  = {Qwen3-32B Olympiad-CP: an open reasoning model for competitive programming},
  author = {Aarav Verma},
  year   = {2026},
  note   = {Independently trained; SFT + GRPO with verifiable test-case reward}
}

trfrasdf/qwen3-32b-olympiad-cp

Qwen3-32B Olympiad-CP (reasoning model for competitive programming)