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Bluebox85033
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Model Details
Model Provider
Bluebox85033
Model Tree
Qwen/Qwen2.5-3B
Input Modalities
Output Modalities
Supported Functionality
pass@1, greedy decoding. Base is the unmodified Qwen2.5-3B evaluated under the same prompts and decoding.
| Benchmark | Base | Cogito-3B | n |
|---|---|---|---|
| Countdown (solve rate) | 7.8% | 64.1% | 64 |
| GSM8K | 81.0% | 82.3% | 300 |
| MATH-500 | 55.0% | 64.3% | 300 |
The largest change is on Countdown, a task under-represented in standard pretraining, where the base model rarely emits a valid solution. GSM8K sits near this base model's ceiling under the given prompt, so the change there is small. Sampled reasoning traces are provided in results/aha_transcripts.md.
Qwen/Qwen2.5-3BAssistant: <think>\n and the model completes … </think> <answer> … </answer>Cogito-3B is a base completion model, not an instruction/chat model. It expects the training prompt format shown below.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Bluebox85033/cogito-3b"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto") def countdown_prompt(nums, target): return ( "A conversation between User and Assistant. The User poses a Countdown number puzzle, and the Assistant solves it.\n" "The Assistant first reasons step by step inside <think> </think> tags, and then gives ONLY the final arithmetic expression inside <answer> </answer> tags.\n" "Rules: use each given number exactly once; the only allowed operations are + - * / and parentheses; the expression must evaluate exactly to the target.\n" "Example answer format: <answer>(3 + 5) * 2</answer>\n\n" f"User: Numbers: {nums}. Target: {target}. Find an expression that uses each number exactly once and equals {target}.\n" "Assistant: <think>\n" ) prompt = countdown_prompt([3, 5, 2], 16)inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False) # greedyprint("<think>\n" + tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))For math, use the same structure with the math template (final answer as \boxed{...}):
python
def math_prompt(problem): return ( "A conversation between User and Assistant. The User asks a math question, and the Assistant solves it.\n" "The Assistant first reasons step by step inside <think> </think> tags, and then gives the final answer inside <answer> </answer> tags, written as \\boxed{...}.\n" "Example answer format: <answer>\\boxed{42}</answer>\n\n" f"User: {problem}\n" "Assistant: <think>\n" )Two-stage curriculum:
(sorted numbers, target). Reward: the model's expression must use each number exactly once with only + - * / ( ), and evaluate exactly to the target.\boxed{} answer equivalence to ground truth.Across steps, mean reward increases and mean completion length grows. Full data generation, reward functions, and training/evaluation code are in the GitHub repository and under recipe/ in this repo.
pass@1 with greedy decoding on held-out splits. The Countdown test set is self-generated and disjoint from training; GSM8K uses the official test split; MATH-500 is HuggingFaceH4/MATH-500. Sample sizes are given in the results table; the Countdown set (n=64) is small and its figure should be read as indicative.
The method (RLVR / GRPO) and the Countdown demonstration follow DeepSeek-R1-Zero and the TinyZero reproduction. This model is a reproduction and extension rather than a new method: it provides an end-to-end reproducible pipeline and a Countdown→math curriculum with a controlled before/after measurement on held-out splits.
bibtex
@misc{cogito3b, title = {Cogito-3B: GRPO reasoning training on Qwen2.5-3B (Countdown and math)}, author = {Bluebox85033}, year = {2026}, howpublished = {\url{https://huggingface.co/Bluebox85033/cogito-3b}}}import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Bluebox85033/cogito-3b"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto") def countdown_prompt(nums, target): return ( "A conversation between User and Assistant. The User poses a Countdown number puzzle, and the Assistant solves it.\n" "The Assistant first reasons step by step inside <think> </think> tags, and then gives ONLY the final arithmetic expression inside <answer> </answer> tags.\n" "Rules: use each given number exactly once; the only allowed operations are + - * / and parentheses; the expression must evaluate exactly to the target.\n" "Example answer format: <answer>(3 + 5) * 2</answer>\n\n" f"User: Numbers: {nums}. Target: {target}. Find an expression that uses each number exactly once and equals {target}.\n" "Assistant: <think>\n" ) prompt = countdown_prompt([3, 5, 2], 16)inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=1024, do_sample=False) # greedyprint("<think>\n" + tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))def math_prompt(problem): return ( "A conversation between User and Assistant. The User asks a math question, and the Assistant solves it.\n" "The Assistant first reasons step by step inside <think> </think> tags, and then gives the final answer inside <answer> </answer> tags, written as \\boxed{...}.\n" "Example answer format: <answer>\\boxed{42}</answer>\n\n" f"User: {problem}\n" "Assistant: <think>\n" )@misc{cogito3b, title = {Cogito-3B: GRPO reasoning training on Qwen2.5-3B (Countdown and math)}, author = {Bluebox85033}, year = {2026}, howpublished = {\url{https://huggingface.co/Bluebox85033/cogito-3b}}}