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#math-reasoning

2 curated events
papersSEP 10 04:00 UTC

Study proposes token-trimming approach to supervised fine-tuning for math reasoning

A new arXiv paper argues that standard supervised fine-tuning applies its loss uniformly across all tokens, even though some are already mastered and others carry far more useful learning signal for mathematical reasoning. The authors introduce a token-trimming perspective that prioritizes which tokens a model should actually learn during fine-tuning, aiming to avoid over-sharpening well-understood tokens while strengthening the ones that matter most.

fine-tuning/math-reasoning/training/sft
papersSEP 12 04:00 UTC

Magenta: Closing the Loop Between Mathematical Reasoning and Lean Verification

A new arXiv paper introduces Magenta, a method that connects informal natural-language mathematical reasoning by large language models with the formal proof assistant Lean. The approach aims to let models generate reasoning in ordinary language while Lean checks correctness, closing the gap between informal and formally verified mathematics.

math-reasoning/lean/llm/verification