A new study, "MathShikkha," investigated the effectiveness of Chain-of-Thought (CoT) supervision for improving mathematical reasoning in small language models (SLMs) specifically for the Bangla language. The research constructed a dataset using GPT-5.4-generated rationales and found that while CoT supervision did not significantly improve in-domain reasoning accuracy for stronger SLMs, it notably enhanced a weaker 4B model. However, on a larger benchmark, CoT significantly outperformed answer-only fine-tuning across all models, preserving or improving out-of-domain accuracy. AI
IMPACT Investigates how different supervision methods impact LLM performance in low-resource languages, offering insights into training strategies.
RANK_REASON Academic paper detailing a controlled study on language model training methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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