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ancient-rl-grpo-pilot3-step50
MooreMuaMu/ancient-rl-grpo-pilot3-step50
ancient-rl-grpo-pilot3-step50 is a machine learning model from MooreMuaMu. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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At a glance
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Jul 20, 2026
- Updated
- Jul 20, 2026
- SHA
- 2481899c
Base models
Model card
From the Hugging Face model README
Ancient Language GRPO — Pilot 3 Step 50
最均衡的 RL checkpoint：10 个 bucket 中 8 个涨点。
模型信息
- 基座模型: Qwen3-14B (zisuh/round2-10lang-passk-15k-sft-0608 checkpoint-195-merged)
- 训练方法: GRPO (Group Relative Policy Optimization) + LoRA rank=64
- Reward: BLEU-2 + BERTScore (batched)
- 训练步数: 50 / 300 (此 checkpoint 在 step 50 保存)
- 超参数: gen=16, temp=1.0, β=0.01, lr=2e-6, constant scheduler
训练 Bucket (10 个)
| Bucket | 任务 | 训练样本数 | test_final_new Δ |
|---|---|---|---|
| tibetan__annotation | 注释 | 5,273 | +0.03pp |
| traditional_mongolian__annotation | 注释 | 2,403 | +1.54pp ✅ |
| manchu__annotation | 注释 | 5,068 | +0.61pp ✅ |
| tangut__segmentation | 断句 | 3,305 | +0.43pp ✅ |
| zhuang__segmentation | 断句 | 222 | -0.92pp |
| uyghur__restoration | 修复 | 4,335 | -1.81pp |
| classical_chinese__translation | 翻译 | 10,000 | +0.35pp ✅ |
| zhuang__translation | 翻译 | 10,000 | -0.58pp |
| traditional_mongolian__translation | 翻译 | 10,000 | +1.06pp ✅ |
| tangut__translation | 翻译 | 10,000 | -0.98pp |
总训练数据: 60,606 samples (每 bucket ≤ 10,000，均衡采样)
评测结果 (sacrebleu sentence-BLEU-2, 0-100)
在 test_final_new 上评测 (50 samples/bucket, n=8, temp=0.7):
| Bucket | Baseline | This Model | Δ |
|---|---|---|---|
| traditional_mongolian / annotation | 57.04 | 58.58 | +1.54pp |
| traditional_mongolian / translation | 29.42 | 30.48 | +1.06pp |
| manchu / annotation | 65.44 | 66.05 | +0.61pp |
| tangut / translation | 42.09 | 41.11 | -0.98pp |
| tangut / segmentation | 81.74 | 82.17 | +0.43pp |
| classical_chinese / translation | 42.04 | 42.39 | +0.35pp |
| tibetan / annotation | 57.73 | 57.25 | -0.48pp |
| zhuang / translation | 51.23 | 50.65 | -0.58pp |
| zhuang / segmentation | 66.67 | 65.75 | -0.92pp |
| uyghur / restoration | 87.58 | 85.77 | -1.81pp |
使用方式
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("MooreMuaMu/ancient-rl-grpo-pilot3-step50", torch_dtype="bfloat16", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("MooreMuaMu/ancient-rl-grpo-pilot3-step50")
关键发现
- β=0.01 让翻译任务获得了涨点（β=0.02 时翻译无改善）
- Step 50 是多数 bucket 的最优 checkpoint，说明早期学习最有效
- 数据均衡（每 bucket ≤ 10k）防止了大 bucket 主导训练
- Type
- qwen3
- License
- apache-2.0
- Languages
- zh, mn, ug, bo, za
- Created
- Jul 20, 2026
- Updated
- Jul 20, 2026