PromptCoT-2.0-SFT-7B

PromptCoT-2.0-SFT-7B

This model is part of PromptCoT 2.0 (Scaling Prompt Synthesis for LLM Reasoning).
It is a 7B parameter model trained entirely on synthetic prompts generated by PromptCoT 2.0, with reasoning trajectories distilled from GPT-OSS-120B (medium).

Unlike prior works (e.g., OpenMathReasoning, OpenCodeReasoning) that rely on human-written prompts, this model demonstrates that fully synthetic data can match or even surpass the effectiveness of manually curated datasets for advancing reasoning in both mathematics and programming.

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📊 Comparison

PromptCoT-2.0-SFT-7B is trained 100% on synthetic prompts with teacher trajectories from GPT-OSS-120B (medium).
Below we compare it against two widely used human-written prompt baselines.

Metric: Pass@1 for AIME24/25, HMMT Feb25, LiveCodeBench v5/v6; Elo for Codeforces.

Takeaways

• Fully synthetic wins: PromptCoT-2.0-SFT-7B outperforms human-written baselines across most math benchmarks and all code benchmarks.
• Scalable & practical: High performance without manual prompt curation suggests a clear path to scaling reasoning with synthetic data.

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🚀 Usage

You can load the model via Hugging Face transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "xl-zhao/PromptCoT-2.0-SFT-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Solve for x: If 2x + 5 = 17, what is the value of x?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📂 Training Details

• Data: 4.8M fully synthetic prompts generated by PromptCoT 2.0
• Teacher: GPT-OSS-120B (medium), used for reasoning trajectory distillation
• Domains: Mathematics (Olympiad-level) and Programming (competitive coding)
• Training regime: Supervised fine-tuning (SFT), 100% synthetic data

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🔮 Key Insights

• Fully synthetic prompts work: No reliance on human-written datasets.
• Compact trajectories: Distilled responses are shorter than those in prior datasets, reducing inference cost while maintaining quality.
• Scalability: Opens the door for training larger reasoning models on purely synthetic corpora.

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📜 Citation

If you use this model or the PromptCoT 2.0 dataset, please cite:

@article{zhao2025promptcot2,
  title     = {PromptCoT 2.0: Scaling Prompt Synthesis for Large Language Model Reasoning},
  author    = {Zhao, Xueliang and Wu, Wei and Guan, Jian and Gong, Zhuocheng and Kong, Lingpeng},
  journal   = {arXiv preprint arXiv:2509.19894},
  year      = {2025},
  url       = {https://arxiv.org/abs/2509.19894}
}