Poro 2 8B Math Reasoning RL Preview Model Card

Poro 2 8B Math Reasoning RL Preview is a specialized model focused on mathematical reasoning and problem-solving. This preview model was created through reinforcement learning (RL) on top of the Math Reasoning SFT checkpoint. This model excels at mathematical reasoning tasks but is not optimized for general conversational use or other domains.

The model produces reasoning traces in both English and Finnish, adapting the language of its reasoning based on the language of the input prompt. The RL training used separate environments for English and Finnish math problems, a novel approach that maintains strong bilingual reasoning performance.

Poro 2 was created in a collaboration between AMD Silo AI, the TurkuNLP group of the University of Turku, and High Performance Language Technologies (HPLT). Training was conducted on the LUMI supercomputer and TensorWave AMD MI325X clusters, using compute resources generously provided by CSC - IT Center for Science, Finland.

For more details on our training and data generation pipeline, check out our Context Extension and Reinforcement Learning Playbook.

Poro 2 Long Model Family

The Poro 2 Long model family extends the Poro 2 models with longer context support and checkpoints trained especially on math reasoning. There are four checkpoints released: a base model, an instruction-tuned model, a math reasoning SFT checkpoint, and the final math reasoning RL checkpoint.

Model Overview

Poro 2 8B Math Reasoning RL Preview is based on the Llama 3.1 8B architecture and has been fine-tuned for mathematical reasoning through supervised fine-tuning followed by reinforcement learning.

Training Process

Context Extension

The base model underwent context extension using LongRoPE scaling to support 131k tokens.

Supervised Fine-Tuning (SFT)

The SFT phase focused on mathematical reasoning and used:

• 2.2M English math reasoning samples from nvidia/Llama-Nemotron-Post-Training-Dataset
• 1.4M Finnish translations of the reasoning samples

Reinforcement Learning (RL)

Following SFT, the model was further trained using reinforcement learning with separate English and Finnish math environments:

• Dual-environment RL training with language-specific math problem sets
• 900 RL training steps

Training Dataset

Post-Training Hyperparameters

SFT

Evaluation Results

Poro 2 8B Math Reasoning RL Preview demonstrates strong mathematical reasoning capabilities in both English and Finnish, showing substantial improvements over both the SFT checkpoint and the original Poro 2 8B Instruct model. On AIME 2025, the model achieves performance comparable to models 6x its size.

All AIME results are avg@64 with temperature 1.0.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "LumiOpen/Llama-Poro-2-8B-Long-Math-Reasoning-RL-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Finnish conversation example
messages = [
    {"role": "user", "content": "Kuinka monta positiivista kokonaislukujakajaa luvulla 196 on?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
)

outputs = model.generate(
    inputs,
    max_new_tokens=32000,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Intended Use

Poro 2 8B Math Reasoning RL Preview is designed for:

• Mathematical problem-solving and reasoning tasks
• Step-by-step mathematical reasoning with explicit reasoning traces
• Math education and tutoring applications
• Competition mathematics (e.g., AIME-level problems)
• Mathematical reasoning in both English and Finnish

Note: This model is optimized specifically for mathematical reasoning and is not suitable for general conversational AI or other domains.

Ethical Considerations and Limitations

Poro 2 8B Math Reasoning RL Preview is a specialized model optimized for mathematical reasoning in English and Finnish. As a preview release, this model has specific limitations:

Key limitations:

• Optimized exclusively for mathematical reasoning; not suitable for general conversation, coding, creative writing, or other domains
• This is an early preview release
• Reasoning traces are in English or Finnish depending on prompt language; limited proficiency in other languages
• May occasionally generate biased, inappropriate, or factually incorrect content

Safety Considerations:

• Users should verify important factual claims independently
• The model should not be used for medical, legal, or financial advice without human oversight
• Responses should be reviewed for appropriateness in sensitive contexts

License

Built with Llama

Poro 2 8B Math Reasoning RL Preview is released under the Llama 3.1 Community License. Please review the license terms before use.

Citation

@misc{poro2_long_2025, title={}, author={}, year={}, howpublished={} }

Acknowledgments

We thank CSC - IT Center for Science, Finland for providing access to the LUMI supercomputer, and TensorWave for providing access to AMD MI325X GPU clusters. This work was supported by the High Performance Language Technologies (HPLT) project and conducted in collaboration with TurkuNLP from the University of Turku. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101070350.