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bowen0815/BRIDGE
BRIDGE is a text generation model from bowen0815. Use it when you need the model to write or continue text. The card lists the license as mit.
Checkpoints for the SDM 2026 paper. BRIDGE is a three-stage curriculum that distills long Chain-of-Thought reasoning from a large teacher into a compact student while improving accuracy and compressing output length.
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Updated Jul 12, 2026
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From the Hugging Face model README
Checkpoints for the SDM 2026 paper. BRIDGE is a three-stage curriculum that distills long Chain-of-Thought reasoning from a large teacher into a compact student while improving accuracy and compressing output length.
qwen2.5-3b/stage3_rewrite_v2/final_model โ Qwen2.5-3B trained with the full BRIDGE pipeline.
On GSM8K it reaches 76.19% accuracy / 167 avg tokens (vs. Base 64.90% / 230), evaluated on
the 1,319-sample test set.
from huggingface_hub import snapshot_download
snapshot_download("bowen0815/BRIDGE",
allow_patterns="qwen2.5-3b/stage3_rewrite_v2/final_model/*")
qwen2.5-3b/ Qwen2.5-3B: stage1, stage2 (best+final), stage3, stage3_rewrite_v2 (FINAL),
stage3_sft, stage4, pure_sft, stage2_G4/G8 ablations
llama-3.2-3b/ Llama-3.2-3B: stage1/2/3 + baselines (pure_sft, short_cot_kd, mix_length_kd, superrl_sft)
qwen2.5-1.5b/ Qwen2.5-1.5B: stage1/2/3_rewrite + superrl (weaker; kept for completeness)
misc/ phase1_unified
archive/ archived completed stage-1 SFT + validation artifacts
data/ teacher (Ollama) GSM8K rollout: gsm8k_full_7473.jsonl (raw, 7460),
original_cot_7k_clean.jsonl (filtered correct, 6170), filter_stats.json
Each model directory is a standard ๐ค Transformers checkpoint (full fine-tuning, not LoRA).
| Method | Accuracy | Avg tokens |
|---|---|---|
| Base | 64.90% | 230 |
| BRIDGE | 76.19% | 167 |
Zero-shot transfer: SVAMP 83.33% / MATH-500 38.20%.
@article{yu2026curriculum,
title={Curriculum Learning for Efficient Chain-of-Thought Distillation via Structure-Aware Masking and GRPO},
author={Yu, Bowen and Wang, Maolin and Zhang, Sheng and Wang, Binhao and Wen, Yi and Gao, Jingtong and Liu, Bowen and Zhao, Zimo and Wang, Wanyu and Zhao, Xiangyu},
journal={arXiv preprint arXiv:2602.17686},
year={2026}
}
License: MIT.