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unsloth/
AceReason-Nemotron-14B

Name: AceReason-Nemotron-14B
Availability: InStock

unsloth - reasoning - math - code - reinforcement learning - pytorch

conversational

2505.16400

nvidia/AceReason-Nemotron-14B

finetune

other

autotrain_compatible

text-generation-inference

endpoints_compatible

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Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning

We're thrilled to introduce AceReason-Nemotron-14B, a math and code reasoning model trained entirely through reinforcement learning (RL), starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025 (+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6 (+7%), and 2024 on Codeforces (+543). We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first RL training on math-only prompts, then RL training on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks, but also code reasoning tasks. In addition, extended code-only RL further improves code benchmark performance while causing minimal degradation in math results. We find that RL not only elicits the foundational reasoning capabilities acquired during pre-training and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable.

We share our training recipe, training logs in our technical report.

Results

We evaluate our model against competitive reasoning models of comparable size within Qwen2.5 and Llama3.1 model family on AIME 2024, AIME 2025, LiveCodeBench v5 (2024/08/01 - 2025/02/01), and LiveCodeBench v6 (2025/02/01-2025/05/01). More evaluation results can be found in our technical report.

Model	AIME 2024 (avg@64)	AIME 2025 (avg@64)	LCB v5 (avg@8)	LCB v6 (avg@8)
QwQ-32B	79.5	65.8	63.4	-
DeepSeek-R1-671B	79.8	70.0	65.9	-
Llama-Nemotron-Ultra-253B	80.8	72.5	66.3	-
o3-mini (medium)	79.6	76.7	67.4	-
Light-R1-14B	74	60.2	57.9	51.5
DeepCoder-14B (32K Inference)	71	56.1	57.9	50.4
OpenMath-Nemotron-14B	76.3	63.0	-	-
OpenCodeReasoning-Nemotron-14B	-	-	59.4	54.1
Llama-Nemotron-Super-49B-v1	67.5	60.0	45.5	-
DeepSeek-R1-Distilled-Qwen-14B	69.7	50.2	53.1	47.9
DeepSeek-R1-Distilled-Qwen-32B	72.6	54.9	57.2	-
AceReason-Nemotron-14B 🤗	78.6	67.4	61.1	54.9

How to use

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'nvidia/AceReason-Nemotron-14B'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

prompt = "Jen enters a lottery by picking $4$ distinct numbers from $S=\\{1,2,3,\\cdots,9,10\\}.$ $4$ numbers are randomly chosen from $S.$ She wins a prize if at least two of her numbers were $2$ of the randomly chosen numbers, and wins the grand prize if all four of her numbers were the randomly chosen numbers. The probability of her winning the grand prize given that she won a prize is $\\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$."
messages = [{"role": "user", "content": prompt}]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to("cuda")

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768,
    temperature=0.6,
    top_p=0.95
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Usage Recommendations

Don't include a system prompt; instead, place all instructions directly in the user prompt.
We recommend using the following instruction for math questions: Please reason step by step, and put your final answer within \boxed{}.
We recommend using the following instruction for code questions: Write Python code to solve the problem. Please place the solution code in the following format:\n ```python\n # Your solution code here\n ```

Correspondence to

Yang Chen (yachen@nvidia.com), Zhuolin Yang (zhuoliny@nvidia.com), Zihan Liu (zihanl@nvidia.com), Chankyu Lee (chankyul@nvidia.com), Wei Ping (wping@nvidia.com)

License

Your use of this model is governed by the NVIDIA Open Model License.

Citation

@article{acereason2025,
  title={AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning},
  author={Chen, Yang and Yang, Zhuolin and Liu, Zihan and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
  journal={arXiv preprint},
  year={2025}
}

unsloth/AceReason-Nemotron-14B