Luth-2: Pushing the French Capabilities of SLMs with MOPD

Luth-2 introduces 0.8B- and 2B-parameter French small language models post-trained from Qwen3.5 to improve performance in mathematics, coding, knowledge, instruction following, multi-turn dialogue, and tool calling while remaining suitable for local deployment. The models were trained first through supervised fine-tuning on a decontaminated 3.1-billion-token French dataset generated and translated from multiple sources, then enhanced through multi-domain on-policy distillation, in which separate reinforcement-learning specialists for math, code, and instruction following were distilled back into one model. Evaluation across 12 French benchmarks, including corrected math tests, translated coding and tool-use tasks, and multilingual knowledge and instruction-following benchmarks, found that Luth-2 generally leads tested models in its parameter classes and remains competitive with substantially larger alternatives. The authors attribute gains to expanded training data, Qwen3.5’s responsiveness to post-training, specialized reinforcement learning, and distillation that preserves gains across domains, and they release the models, datasets, code, training recipe, and evaluation setup.

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