This is an ablation, not one of the headline models: it is the same level trained under a different reward, published so the reward-design comparison in the paper can be rerun rather than taken on faith. For the main model at this level see ssurface/cot-dialect-olmo3-7b-think-grpo-l2.

Results

	Accuracy
This adapter	81.8%

GSM8K test (n=1317), greedy decoding, single-turn, no exemplars, no self-consistency.

Training data

GSM8K train, re-expressed at level L2 by a teacher model: 6950 examples, median chain length 140 characters inside <think>.

Across the family the median chain runs from 532 characters at L1 to 16 at L5 — a 33x span. An L2 chain looks like this:

- Sandoval: 12
- Hawkins: 12 / 2 = 6
- Sloan: 12 + 10 = 22
- Total: 12 + 6 + 22 = 40

Training setup

GRPO on top of the merged level-2 SFT model.


Engine	`trl.GRPOTrainer` on stock `transformers`, attention `sdpa`
Reward	`correctness`, `format`, `early_solve`
Loss type	dapo
Generations per prompt	8
Batch	64 x 1 accum
Max completion	256 tokens
Learning rate	1e-05
KL coefficient (beta)	0.0
Prompt set	`gsm8k_grpo_balanced_1k.json`
Trained on	`merged_olmo/l2`
LoRA	r=16, alpha=32
Hardware	1x NVIDIA A100 80GB

Reward components

correctness — +/- the gold solution's step count on an answer match, so harder problems are worth more
format — the response must be one <think>...</think> block then #### <answer>
early_solve — rewards reaching the answer early in the span rather than late

Engine note. Stock transformers with sdpa attention, not a fused-kernel wrapper. The fused path produced adapters whose lora_B matrices were all zero — mathematically inert despite loading without error. Every adapter in this collection was verified lora_B != 0 before publishing; 13 that failed that check were withheld.

Usage

Solve this using Level 2 (Concise).
Problem: {your problem}

Stacks on the SFT model, not the raw base. Trained against the merged SFT model, so loading it straight onto allenai/Olmo-3-7B-Think will not reproduce the number above.

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(model, "ssurface/cot-dialect-olmo3-7b-think-sft-l2")   # 1. SFT for this level
model = model.merge_and_unload()
model = PeftModel.from_pretrained(model, "ssurface/cot-dialect-olmo3-7b-think-grpo-early-solve-l2")   # 2. this adapter
tok = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think")

Limitations

Trained and evaluated on math word problems only.
Accuracy falls with problem difficulty, fastest at the compressed levels.
Single seed unless the repo name says otherwise; differences of a couple of points are within noise (95% half-width ~2.7 pp at n=1317, ~4.4 pp at n=500).
Ablation artefact. It was trained to answer one question about reward design and may be worse than the core model at the same level.

Citation

bibtex

@misc{cot-compression-dialects,
  title  = {Chain-of-Thought Compression Dialects},
  author = {Frolov, Anatolii},
  year   = {2026}
}

ssurface/cot-dialect-olmo3-7b-think-grpo-early-solve-l2

Olmo-3-7B-Think — L2 dialect (Structured shorthand) · reward ablation early-solve