efficient-reasoning
Collection
Models from "Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability". • 9 items • Updated
olmo3-7b-think-glp
Paper: Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability
Collection: Efficient Reasoning: CoT Faithfulness & Monitorability
Method: Within-group length penalty from Kimi k1.5 (Kimi Team, 2025)
Hyperparameters
| Base model | allenai/Olmo-3-7B-Think |
| Method | Within-Group Length Penalty (GLP) |
| Released as | LoRA adapter, step 100 |
| Length-reward weight / warm-up | 0.1 / 10 steps |
| LoRA | rank 16, alpha 32, dropout 0, all attention and MLP projections |
| Optimiser | AdamW, learning rate 1e-5, KL coefficient 0 |
| Batch | 32 prompts x 16 generations |
| Sampling | temperature 0.8, top-p 0.95 |
| Data | numina_amc_aime (PRIME Eurus-2-RL-Data), ~2,223 prompts |
| Checkpoint selection | AIME22, every 50 steps |
| Hardware | 2x NVIDIA GH200 |
Length-penalty warm-up was 10 steps for this model (100 for the Qwen3 models).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think", torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think")
model = PeftModel.from_pretrained(base, "Samll/olmo3-7b-think-glp")
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