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DGurgurov/SmolLM3-3B-SFT-GRPO-ES
SmolLM3-3B-SFT-GRPO-ES is a reinforcement learning model from DGurgurov. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
This model is the result of the second stage of the ReasonXL two-stage reasoning adaptation pipeline applied to HuggingFaceTB/SmolLM3-3B.
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From the Hugging Face model README
This model is the result of the second stage of the ReasonXL two-stage reasoning adaptation pipeline applied to HuggingFaceTB/SmolLM3-3B.
The first stage shifts the model's reasoning language from English to Spanish through supervised fine-tuning on reasoning traces from toroe/ReasonXL-SFT.
The corresponding SFT model is:
This model applies RL (Dr. GRPO) to the corresponding SFT model.
The objective is to recover reasoning quality lost during supervised fine-tuning while preserving compliance with the target reasoning language.
Training uses a composite reward over verifiable mathematical problems.
HuggingFaceTB/SmolLM3-3Btoroe/ReasonXL-SFTDGurgurov/SmolLM3-3B-SFT-ESFull training details, reward formulation, evaluation results, and methodology will follow soon.
If you use this model, please cite:
@misc{gurgurov2026reasonxlshiftingllmreasoning,
title={ReasonXL: Shifting LLM Reasoning Language Without Sacrificing Performance},
author={Daniil Gurgurov and Tom Röhr and Sebastian von Rohrscheidt and Josef van Genabith and Alexander Löser and Simon Ostermann},
year={2026},
eprint={2604.12378},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2604.12378},
}